mongoDB support

pas2_fork/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.xz filter=lfs diff=lfs merge=lfs -text
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pas2_fork/.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Logs
+*.log
+
+# Local configuration
+.env
+
+# Results and data files
+results/
+*.csv
+*.xlsx
+
+# Gradio
+flagged/
+
+# Code outputs
+*.png
+*.xlsx
+
+.gradio/
+.ipynb_checkpoints/
+
+# Environment Variables
+.env
+
+# IDEs and Editors
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# Operating System Files
+.DS_Store
+Thumbs.db
+
+# Build files
+build/
+dist/
+*.egg-info/
+
+# Database files
+*.db
+*.sqlite3
+
+# Backup files
+*.bak
+
+# Other sensitive or project-specific files
+config.ini
+secrets.json
+credentials.yml

pas2_fork/CLAUDE.md

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+# LLM Hallucination Detector Guidelines
+
+## Commands
+- Setup: `pip install -r requirements.txt`
+- Configure: Set environment variables `HF_MISTRAL_API_KEY` and `HF_OPENAI_API_KEY`
+- Run: `python app.py`
+- Lint: `ruff check app.py`
+- Format: `black app.py`
+- Type check: `mypy app.py`
+
+## Code Style
+- Follow PEP 8 conventions with 4-space indentation
+- Use type hints with Pydantic for data validation
+- Write descriptive docstrings using triple quotes
+- Name variables/functions in snake_case, classes in PascalCase
+- Organize imports: stdlib first, then third-party, then local
+- Exception handling: use try/except blocks with specific exceptions
+- Constants should be UPPERCASE and defined at class/module level
+- Prefer f-strings over other string formatting methods
+
+## Architecture
+- App uses Gradio for UI, SQLite for persistence
+- LLM integration with Mistral Large and OpenAI o3-mini
+- Paraphrase-based approach for hallucination detection
+- Maintain clean separation between UI and backend logic

pas2_fork/LICENSE

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+MIT License with Attribution Requirement
+
+Copyright (c) 2024 Serhan Yilmaz, Sabanci University
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+1. The above copyright notice and this permission notice shall be included in all
+   copies or substantial portions of the Software.
+
+2. Any use of the Software must include appropriate credit to the original authors
+   by citing the project as follows:
+
+   Serhan Yilmaz. (2024). PAS2 - Paraphrase-based AI System for Semantic 
+   Similarity. https://github.com/serhanylmz/pas2
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

pas2_fork/README.md

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+---
+title: Pas2 Llm Hallucination Detector
+emoji: 🐠
+colorFrom: purple
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.20.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: pas2 is an llm-as-a-judge system used to verify outputs
+---
+
+# PAS2 - Hallucination Detection System
+
+A sophisticated system for detecting hallucinations in AI responses using a paraphrase-based approach with model-as-judge verification.
+
+## Features
+
+- **Paraphrase Generation**: Automatically generates semantically equivalent variations of user queries
+- **Multi-Model Architecture**: Uses Mistral Large for responses and OpenAI's o3-mini as a judge
+- **Real-time Progress Tracking**: Visual feedback during the analysis process
+- **Permanent Cloud Storage**: User feedback and results are stored in MongoDB Atlas for persistent storage across restarts
+- **Interactive Web Interface**: Clean, responsive Gradio interface with example queries
+- **Detailed Analysis**: Provides confidence scores, reasoning, and specific conflicting facts
+- **Statistics Dashboard**: Real-time tracking of hallucination detection statistics
+
+## Setup
+
+1. Clone this repository
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. Set up your API keys as environment variables:
+   - `HF_MISTRAL_API_KEY`: Your Mistral AI API key
+   - `HF_OPENAI_API_KEY`: Your OpenAI API key
+
+## Deployment on Hugging Face Spaces
+
+1. Create a new Space on Hugging Face
+2. Select "Gradio" as the SDK
+3. Add your repository
+4. Set up a MongoDB Atlas database (see below)
+5. Set the following secrets in your Space's settings:
+   - `HF_MISTRAL_API_KEY`
+   - `HF_OPENAI_API_KEY`
+   - `MONGODB_URI`
+
+### MongoDB Atlas Setup
+
+For permanent data storage that persists across HuggingFace Space restarts:
+
+1. Create a free [MongoDB Atlas account](https://www.mongodb.com/cloud/atlas/register)
+2. Create a new cluster (the free tier is sufficient)
+3. In the "Database Access" menu, create a database user with read/write permissions
+4. In the "Network Access" menu, add IP `0.0.0.0/0` to allow access from anywhere (required for HuggingFace Spaces)
+5. In the "Databases" section, click "Connect" and choose "Connect your application"
+6. Copy the connection string and replace `<password>` with your database user's password
+7. Set this as your `MONGODB_URI` secret in HuggingFace Spaces settings
+
+## Usage
+
+1. Enter a factual question or select from example queries
+2. Click "Detect Hallucinations" to start the analysis
+3. Review the detailed results:
+   - Hallucination detection status
+   - Confidence score
+   - Original and paraphrased responses
+   - Detailed reasoning and analysis
+4. Provide feedback to help improve the system
+
+## How It Works
+
+1. **Query Processing**:
+   - Your question is paraphrased multiple ways
+   - Each version is sent to Mistral Large
+   - Responses are collected and compared
+
+2. **Hallucination Detection**:
+   - OpenAI's o3-mini analyzes responses
+   - Identifies factual inconsistencies
+   - Provides confidence scores and reasoning
+
+3. **Feedback Collection**:
+   - User feedback is stored in MongoDB Atlas
+   - Cloud-based persistent storage ensures data survival
+   - Statistics are updated in real-time
+   - Data can be exported for further analysis
+
+## Data Persistence
+
+The application uses MongoDB Atlas for data storage, providing several benefits:
+- **Permanent Storage**: Data persists even when Hugging Face Spaces restart
+- **Scalability**: MongoDB scales as your data grows
+- **Cloud-based**: No reliance on Space-specific storage that can be lost
+- **Query Capabilities**: Powerful query functionality for data analysis
+- **Export Options**: Built-in methods to export data to CSV
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit pull requests.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## About
+
+This application uses a combination of paraphrasing techniques and model-as-judge approaches to identify potential hallucinations in LLM responses. It provides confidence scores, identifies conflicting facts, and offers detailed reasoning for its judgments.
+
+## Features
+
+- Generates paraphrased versions of input queries
+- Evaluates responses using semantic similarity analysis
+- Provides match percentage and similarity metrics
+- Includes visualization tools for similarity matrices
+- Web interface for interactive testing
+- Benchmarking capabilities for bulk evaluation
+
+## Installation
+
+```bash
+git clone https://github.com/serhanylmz/pas2
+cd pas2
+pip install -r requirements.txt
+```
+
+Set up your OpenAI API key in a `.env` file:
+```
+OPENAI_API_KEY=your_api_key_here
+```
+
+## Usage
+
+### Web Interface
+
+Run the Gradio interface:
+```bash
+python pas2-gradio.py
+```
+
+### Benchmark Tool
+
+Run the benchmark tool:
+```bash
+python pas2-benchmark.py --json_file your_data.json --num_samples 10
+```
+
+### Library Usage
+
+```python
+from pas2 import PAS2
+
+detector = PAS2()
+hallucinated, response, questions, answers = detector.detect_hallucination(
+    "your question",
+    n_paraphrases=5,
+    similarity_threshold=0.9,
+    match_percentage_threshold=0.7
+)
+```
+
+## Configuration
+
+- Default model: gpt-4-2024-08-06
+- Default embedding model: text-embedding-3-small
+- Adjustable similarity and match percentage thresholds
+
+## Output Files
+
+- Similarity matrix plots (PNG)
+- Match matrix plots (PNG)
+- Benchmark results (CSV, TXT)
+- User feedback logs (XLSX)
+
+## License
+
+This project is licensed under the MIT License with an attribution requirement - see the [LICENSE](LICENSE) file for details.
+
+### Citation
+
+If you use PAS2 in your research or project, please cite it as:
+
+```bibtex
+@software{pas2_2024,
+  author = {Serhan Yilmaz},
+  title = {PAS2 - Paraphrase-based AI System for Semantic Similarity},
+  year = {2024},
+  publisher = {GitHub},
+  url = {https://github.com/serhanylmz/pas2}
+}
+```
+
+### Attribution Requirements
+
+When using PAS2, you must provide appropriate attribution by:
+
+1. Including the copyright notice and license in any copy or substantial portion of the software
+2. Citing the project in any publications, presentations, or documentation that uses or builds upon this work
+3. Maintaining a link to the original repository in any forks or derivative works
+
+## Contact
+
+Serhan Yilmaz
+serhan.yilmaz@sabanciuniv.edu
+Sabanci University

pas2_fork/app.py

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+import os
+import gradio as gr
+import pandas as pd
+from datetime import datetime
+from pydantic import BaseModel, Field
+from typing import List, Dict, Any, Optional
+import numpy as np
+from mistralai import Mistral
+from openai import OpenAI
+import re
+import json
+import logging
+import time
+import concurrent.futures
+from concurrent.futures import ThreadPoolExecutor
+import threading
+import pymongo
+from pymongo import MongoClient
+from bson.objectid import ObjectId
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s [%(levelname)s] %(message)s',
+    handlers=[
+        logging.StreamHandler()
+    ]
+)
+
+logger = logging.getLogger(__name__)
+
+class HallucinationJudgment(BaseModel):
+    hallucination_detected: bool = Field(description="Whether a hallucination is detected across the responses")
+    confidence_score: float = Field(description="Confidence score between 0-1 for the hallucination judgment")
+    conflicting_facts: List[Dict[str, Any]] = Field(description="List of conflicting facts found in the responses")
+    reasoning: str = Field(description="Detailed reasoning for the judgment")
+    summary: str = Field(description="A summary of the analysis")
+
+class PAS2:
+    """Paraphrase-based Approach for LLM Systems - Using llm-as-judge methods"""
+    
+    def __init__(self, mistral_api_key=None, openai_api_key=None, progress_callback=None):
+        """Initialize the PAS2 with API keys"""
+        # For Hugging Face Spaces, we prioritize getting API keys from HF_* environment variables
+        # which are set from the Secrets tab in the Space settings
+        self.mistral_api_key = mistral_api_key or os.environ.get("HF_MISTRAL_API_KEY") or os.environ.get("MISTRAL_API_KEY")
+        self.openai_api_key = openai_api_key or os.environ.get("HF_OPENAI_API_KEY") or os.environ.get("OPENAI_API_KEY")
+        self.progress_callback = progress_callback
+        
+        if not self.mistral_api_key:
+            raise ValueError("Mistral API key is required. Set it via HF_MISTRAL_API_KEY in Hugging Face Spaces secrets or pass it as a parameter.")
+        
+        if not self.openai_api_key:
+            raise ValueError("OpenAI API key is required. Set it via HF_OPENAI_API_KEY in Hugging Face Spaces secrets or pass it as a parameter.")
+        
+        self.mistral_client = Mistral(api_key=self.mistral_api_key)
+        self.openai_client = OpenAI(api_key=self.openai_api_key)
+        
+        self.mistral_model = "mistral-large-latest"
+        self.openai_model = "o3-mini"
+        
+        logger.info("PAS2 initialized with Mistral model: %s and OpenAI model: %s", 
+                   self.mistral_model, self.openai_model)
+    
+    def generate_paraphrases(self, query: str, n_paraphrases: int = 3) -> List[str]:
+        """Generate paraphrases of the input query using Mistral API"""
+        logger.info("Generating %d paraphrases for query: %s", n_paraphrases, query)
+        start_time = time.time()
+        
+        messages = [
+            {
+                "role": "system",
+                "content": f"You are an expert at creating semantically equivalent paraphrases. Generate {n_paraphrases} different paraphrases of the given query that preserve the original meaning but vary in wording and structure. Return a JSON array of strings, each containing one paraphrase."
+            },
+            {
+                "role": "user",
+                "content": query
+            }
+        ]
+        
+        try:
+            logger.info("Sending paraphrase generation request to Mistral API...")
+            response = self.mistral_client.chat.complete(
+                model=self.mistral_model,
+                messages=messages,
+                response_format={"type": "json_object"}
+            )
+            
+            content = response.choices[0].message.content
+            logger.debug("Received raw paraphrase response: %s", content)
+            
+            paraphrases_data = json.loads(content)
+            
+            # Handle different possible JSON structures
+            if isinstance(paraphrases_data, dict) and "paraphrases" in paraphrases_data:
+                paraphrases = paraphrases_data["paraphrases"]
+            elif isinstance(paraphrases_data, dict) and "results" in paraphrases_data:
+                paraphrases = paraphrases_data["results"]
+            elif isinstance(paraphrases_data, list):
+                paraphrases = paraphrases_data
+            else:
+                # Try to extract a list from any field
+                for key, value in paraphrases_data.items():
+                    if isinstance(value, list) and len(value) > 0:
+                        paraphrases = value
+                        break
+                else:
+                    logger.warning("Could not extract paraphrases from response: %s", content)
+                    raise ValueError(f"Could not extract paraphrases from response: {content}")
+            
+            # Ensure we have the right number of paraphrases
+            paraphrases = paraphrases[:n_paraphrases]
+            
+            # Add the original query as the first item
+            all_queries = [query] + paraphrases
+            
+            elapsed_time = time.time() - start_time
+            logger.info("Generated %d paraphrases in %.2f seconds", len(paraphrases), elapsed_time)
+            for i, p in enumerate(paraphrases, 1):
+                logger.info("Paraphrase %d: %s", i, p)
+            
+            return all_queries
+            
+        except Exception as e:
+            logger.error("Error generating paraphrases: %s", str(e), exc_info=True)
+            # Return original plus simple paraphrases as fallback
+            fallback_paraphrases = [
+                query,
+                f"Could you tell me about {query.strip('?')}?",
+                f"I'd like to know: {query}",
+                f"Please provide information on {query.strip('?')}."
+            ][:n_paraphrases+1]
+            
+            logger.info("Using fallback paraphrases due to error")
+            for i, p in enumerate(fallback_paraphrases[1:], 1):
+                logger.info("Fallback paraphrase %d: %s", i, p)
+                
+            return fallback_paraphrases
+    
+    def _get_single_response(self, query: str, index: int = None) -> str:
+        """Get a single response from Mistral API for a query"""
+        try:
+            query_description = f"Query {index}: {query}" if index is not None else f"Query: {query}"
+            logger.info("Getting response for %s", query_description)
+            start_time = time.time()
+            
+            messages = [
+                {
+                    "role": "system",
+                    "content": "You are a helpful AI assistant. Provide accurate, factual information in response to questions."
+                },
+                {
+                    "role": "user",
+                    "content": query
+                }
+            ]
+            
+            response = self.mistral_client.chat.complete(
+                model=self.mistral_model,
+                messages=messages
+            )
+            
+            result = response.choices[0].message.content
+            elapsed_time = time.time() - start_time
+            
+            logger.info("Received response for %s (%.2f seconds)", query_description, elapsed_time)
+            logger.debug("Response content for %s: %s", query_description, result[:100] + "..." if len(result) > 100 else result)
+            
+            return result
+            
+        except Exception as e:
+            error_msg = f"Error getting response for query '{query}': {e}"
+            logger.error(error_msg, exc_info=True)
+            return f"Error: Failed to get response for this query."
+    
+    def get_responses(self, queries: List[str]) -> List[str]:
+        """Get responses from Mistral API for each query in parallel"""
+        logger.info("Getting responses for %d queries in parallel", len(queries))
+        start_time = time.time()
+        
+        # Use ThreadPoolExecutor for parallel API calls
+        with ThreadPoolExecutor(max_workers=min(len(queries), 5)) as executor:
+            # Submit tasks and map them to their original indices
+            future_to_index = {
+                executor.submit(self._get_single_response, query, i): i 
+                for i, query in enumerate(queries)
+            }
+            
+            # Prepare a list with the correct length
+            responses = [""] * len(queries)
+            
+            # Counter for completed responses
+            completed_count = 0
+            
+            # Collect results as they complete
+            for future in concurrent.futures.as_completed(future_to_index):
+                index = future_to_index[future]
+                try:
+                    responses[index] = future.result()
+                    
+                    # Update completion count and report progress
+                    completed_count += 1
+                    if self.progress_callback:
+                        self.progress_callback("responses_progress", 
+                                            completed_responses=completed_count, 
+                                            total_responses=len(queries))
+                        
+                except Exception as e:
+                    logger.error("Error processing response for index %d: %s", index, str(e))
+                    responses[index] = f"Error: Failed to get response for query {index}."
+                    
+                    # Still update completion count even for errors
+                    completed_count += 1
+                    if self.progress_callback:
+                        self.progress_callback("responses_progress", 
+                                            completed_responses=completed_count, 
+                                            total_responses=len(queries))
+        
+        elapsed_time = time.time() - start_time
+        logger.info("Received all %d responses in %.2f seconds total", len(responses), elapsed_time)
+        
+        return responses
+    
+    def detect_hallucination(self, query: str, n_paraphrases: int = 3) -> Dict:
+        """
+        Detect hallucinations by comparing responses to paraphrased queries using a judge model
+        
+        Returns:
+            Dict containing hallucination judgment and all responses
+        """
+        logger.info("Starting hallucination detection for query: %s", query)
+        start_time = time.time()
+        
+        # Report progress
+        if self.progress_callback:
+            self.progress_callback("starting", query=query)
+            
+        # Generate paraphrases
+        logger.info("Step 1: Generating paraphrases")
+        if self.progress_callback:
+            self.progress_callback("generating_paraphrases", query=query)
+            
+        all_queries = self.generate_paraphrases(query, n_paraphrases)
+        
+        if self.progress_callback:
+            self.progress_callback("paraphrases_complete", query=query, count=len(all_queries))
+        
+        # Get responses to all queries
+        logger.info("Step 2: Getting responses to all %d queries", len(all_queries))
+        if self.progress_callback:
+            self.progress_callback("getting_responses", query=query, total=len(all_queries))
+        
+        all_responses = []
+        for i, q in enumerate(all_queries):
+            logger.info("Getting response %d/%d for query: %s", i+1, len(all_queries), q)
+            if self.progress_callback:
+                self.progress_callback("responses_progress", query=query, completed=i, total=len(all_queries))
+            
+            response = self._get_single_response(q, index=i)
+            all_responses.append(response)
+        
+        if self.progress_callback:
+            self.progress_callback("responses_complete", query=query)
+        
+        # Judge the responses for hallucinations
+        logger.info("Step 3: Judging for hallucinations")
+        if self.progress_callback:
+            self.progress_callback("judging", query=query)
+        
+        # The first query is the original, rest are paraphrases
+        original_query = all_queries[0]
+        original_response = all_responses[0]
+        paraphrased_queries = all_queries[1:] if len(all_queries) > 1 else []
+        paraphrased_responses = all_responses[1:] if len(all_responses) > 1 else []
+        
+        # Judge the responses
+        judgment = self.judge_hallucination(
+            original_query=original_query,
+            original_response=original_response,
+            paraphrased_queries=paraphrased_queries,
+            paraphrased_responses=paraphrased_responses
+        )
+        
+        # Assemble the results
+        results = {
+            "original_query": original_query,
+            "original_response": original_response,
+            "paraphrased_queries": paraphrased_queries,
+            "paraphrased_responses": paraphrased_responses,
+            "hallucination_detected": judgment.hallucination_detected,
+            "confidence_score": judgment.confidence_score,
+            "conflicting_facts": judgment.conflicting_facts,
+            "reasoning": judgment.reasoning,
+            "summary": judgment.summary
+        }
+        
+        # Report completion
+        if self.progress_callback:
+            self.progress_callback("complete", query=query)
+            
+        logger.info("Hallucination detection completed in %.2f seconds", time.time() - start_time)
+        return results
+    
+    def judge_hallucination(self, 
+                           original_query: str, 
+                           original_response: str, 
+                           paraphrased_queries: List[str], 
+                           paraphrased_responses: List[str]) -> HallucinationJudgment:
+        """
+        Use OpenAI's o3-mini as a judge to detect hallucinations in the responses
+        """
+        logger.info("Judging hallucinations with OpenAI's %s model", self.openai_model)
+        start_time = time.time()
+        
+        # Prepare the context for the judge
+        context = f"""
+Original Question: {original_query}
+
+Original Response: 
+{original_response}
+
+Paraphrased Questions and their Responses:
+"""
+        
+        for i, (query, response) in enumerate(zip(paraphrased_queries, paraphrased_responses), 1):
+            context += f"\nParaphrased Question {i}: {query}\n\nResponse {i}:\n{response}\n"
+        
+        system_prompt = """
+You are a judge evaluating whether an AI is hallucinating across different responses to semantically equivalent questions.
+Analyze all responses carefully to identify any factual inconsistencies or contradictions.
+Focus on factual discrepancies, not stylistic differences.
+A hallucination is when the AI states different facts in response to questions that are asking for the same information.
+
+Your response should be a JSON with the following fields:
+- hallucination_detected: boolean indicating whether hallucinations were found
+- confidence_score: number between 0 and 1 representing your confidence in the judgment
+- conflicting_facts: an array of objects describing any conflicting information found
+- reasoning: detailed explanation for your judgment
+- summary: a concise summary of your analysis
+"""
+
+        try:
+            logger.info("Sending judgment request to OpenAI API...")
+            response = self.openai_client.chat.completions.create(
+                model=self.openai_model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": f"Evaluate these responses for hallucinations:\n\n{context}"}
+                ],
+                response_format={"type": "json_object"}
+            )
+            
+            result_json = json.loads(response.choices[0].message.content)
+            logger.debug("Received judgment response: %s", result_json)
+            
+            # Create the HallucinationJudgment object from the JSON response
+            judgment = HallucinationJudgment(
+                hallucination_detected=result_json.get("hallucination_detected", False),
+                confidence_score=result_json.get("confidence_score", 0.0),
+                conflicting_facts=result_json.get("conflicting_facts", []),
+                reasoning=result_json.get("reasoning", "No reasoning provided."),
+                summary=result_json.get("summary", "No summary provided.")
+            )
+            
+            elapsed_time = time.time() - start_time
+            logger.info("Judgment completed in %.2f seconds", elapsed_time)
+            
+            return judgment
+            
+        except Exception as e:
+            logger.error("Error in hallucination judgment: %s", str(e), exc_info=True)
+            # Return a fallback judgment
+            return HallucinationJudgment(
+                hallucination_detected=False,
+                confidence_score=0.0,
+                conflicting_facts=[],
+                reasoning="Failed to obtain judgment from the model.",
+                summary="Analysis failed due to API error."
+            )
+
+
+class HallucinationDetectorApp:
+    def __init__(self):
+        self.pas2 = None
+        logger.info("Initializing HallucinationDetectorApp")
+        self._initialize_database()
+        self.progress_callback = None
+    
+    def _initialize_database(self):
+        """Initialize MongoDB connection for persistent feedback storage"""
+        try:
+            # Get MongoDB connection string from environment variable
+            mongo_uri = os.environ.get("MONGODB_URI")
+            
+            if not mongo_uri:
+                logger.warning("MONGODB_URI not found in environment variables. Please set it in HuggingFace Spaces secrets.")
+                logger.warning("Using a placeholder URI for now - connection will fail until proper URI is provided.")
+                # Use a placeholder - this will fail but allows the app to initialize
+                mongo_uri = "mongodb+srv://username:password@cluster.mongodb.net/?retryWrites=true&w=majority"
+            
+            # Connect to MongoDB
+            self.mongo_client = MongoClient(mongo_uri)
+            
+            # Access or create database
+            self.db = self.mongo_client["hallucination_detector"]
+            
+            # Access or create collection
+            self.feedback_collection = self.db["feedback"]
+            
+            # Create index on timestamp for faster querying
+            self.feedback_collection.create_index("timestamp")
+            
+            # Test connection
+            self.mongo_client.admin.command('ping')
+            logger.info("MongoDB connection successful")
+            
+        except Exception as e:
+            logger.error(f"Error initializing MongoDB: {str(e)}", exc_info=True)
+            logger.warning("Proceeding without database connection. Data will not be saved persistently.")
+            self.mongo_client = None
+            self.db = None
+            self.feedback_collection = None
+    
+    def set_progress_callback(self, callback):
+        """Set the progress callback function"""
+        self.progress_callback = callback
+    
+    def initialize_api(self, mistral_api_key, openai_api_key):
+        """Initialize the PAS2 with API keys"""
+        try:
+            logger.info("Initializing PAS2 with API keys")
+            self.pas2 = PAS2(
+                mistral_api_key=mistral_api_key, 
+                openai_api_key=openai_api_key,
+                progress_callback=self.progress_callback
+            )
+            logger.info("API initialization successful")
+            return "API keys set successfully! You can now use the application."
+        except Exception as e:
+            logger.error("Error initializing API: %s", str(e), exc_info=True)
+            return f"Error initializing API: {str(e)}"
+    
+    def process_query(self, query: str):
+        """Process the query using PAS2"""
+        if not self.pas2:
+            logger.error("PAS2 not initialized")
+            return {
+                "error": "Please set API keys first before processing queries."
+            }
+        
+        if not query.strip():
+            logger.warning("Empty query provided")
+            return {
+                "error": "Please enter a query."
+            }
+        
+        try:
+            # Set the progress callback if needed
+            if self.progress_callback and self.pas2.progress_callback != self.progress_callback:
+                self.pas2.progress_callback = self.progress_callback
+                
+            # Process the query
+            logger.info("Processing query with PAS2: %s", query)
+            results = self.pas2.detect_hallucination(query)
+            logger.info("Query processing completed successfully")
+            return results
+        except Exception as e:
+            logger.error("Error processing query: %s", str(e), exc_info=True)
+            return {
+                "error": f"Error processing query: {str(e)}"
+            }
+    
+    def save_feedback(self, results, feedback):
+        """Save results and user feedback to MongoDB"""
+        try:
+            logger.info("Saving user feedback: %s", feedback)
+            
+            if self.feedback_collection is None:
+                logger.error("MongoDB connection not available. Cannot save feedback.")
+                return "Database connection not available. Feedback not saved."
+            
+            # Prepare document for MongoDB
+            document = {
+                "timestamp": datetime.now(),
+                "original_query": results.get('original_query', ''),
+                "original_response": results.get('original_response', ''),
+                "paraphrased_queries": results.get('paraphrased_queries', []),
+                "paraphrased_responses": results.get('paraphrased_responses', []),
+                "hallucination_detected": results.get('hallucination_detected', False),
+                "confidence_score": results.get('confidence_score', 0.0),
+                "conflicting_facts": results.get('conflicting_facts', []),
+                "reasoning": results.get('reasoning', ''),
+                "summary": results.get('summary', ''),
+                "user_feedback": feedback
+            }
+            
+            # Insert document into collection
+            self.feedback_collection.insert_one(document)
+            
+            logger.info("Feedback saved successfully to MongoDB")
+            return "Feedback saved successfully!"
+        except Exception as e:
+            logger.error("Error saving feedback: %s", str(e), exc_info=True)
+            return f"Error saving feedback: {str(e)}"
+            
+    def get_feedback_stats(self):
+        """Get statistics about collected feedback from MongoDB"""
+        try:
+            if self.feedback_collection is None:
+                logger.error("MongoDB connection not available. Cannot get feedback stats.")
+                return None
+            
+            # Get total feedback count
+            total_count = self.feedback_collection.count_documents({})
+            
+            # Get hallucination detection stats using aggregation
+            hallucination_pipeline = [
+                {"$group": {
+                    "_id": "$hallucination_detected", 
+                    "count": {"$sum": 1}
+                }}
+            ]
+            detection_stats = {doc["_id"]: doc["count"] 
+                              for doc in self.feedback_collection.aggregate(hallucination_pipeline)}
+            
+            # Get average confidence score
+            avg_pipeline = [
+                {"$group": {
+                    "_id": None,
+                    "average": {"$avg": "$confidence_score"}
+                }}
+            ]
+            avg_result = list(self.feedback_collection.aggregate(avg_pipeline))
+            avg_confidence = avg_result[0]["average"] if avg_result else 0
+            
+            return {
+                "total_feedback": total_count,
+                "hallucinations_detected": detection_stats.get(True, 0),
+                "no_hallucinations": detection_stats.get(False, 0),
+                "average_confidence": round(avg_confidence, 2)
+            }
+        except Exception as e:
+            logger.error("Error getting feedback stats: %s", str(e), exc_info=True)
+            return None
+    
+    def export_data_to_csv(self, filepath=None):
+        """Export all feedback data to a CSV file for analysis"""
+        try:
+            if self.feedback_collection is None:
+                logger.error("MongoDB connection not available. Cannot export data.")
+                return "Database connection not available. Cannot export data."
+            
+            # Query all feedback data
+            cursor = self.feedback_collection.find({})
+            
+            # Convert cursor to list of dictionaries
+            records = list(cursor)
+            
+            # Convert MongoDB documents to pandas DataFrame
+            # Handle nested arrays and complex objects
+            for record in records:
+                # Convert ObjectId to string
+                record['_id'] = str(record['_id'])
+                
+                # Convert datetime objects to string
+                if 'timestamp' in record:
+                    record['timestamp'] = record['timestamp'].strftime("%Y-%m-%d %H:%M:%S")
+                
+                # Convert lists to strings for CSV storage
+                if 'paraphrased_queries' in record:
+                    record['paraphrased_queries'] = json.dumps(record['paraphrased_queries'])
+                if 'paraphrased_responses' in record:
+                    record['paraphrased_responses'] = json.dumps(record['paraphrased_responses'])
+                if 'conflicting_facts' in record:
+                    record['conflicting_facts'] = json.dumps(record['conflicting_facts'])
+            
+            # Create DataFrame
+            df = pd.DataFrame(records)
+            
+            # Define default filepath if not provided
+            if not filepath:
+                filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 
+                                       f"hallucination_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv")
+            
+            # Export to CSV
+            df.to_csv(filepath, index=False)
+            logger.info(f"Data successfully exported to {filepath}")
+            
+            return filepath
+        except Exception as e:
+            logger.error(f"Error exporting data: {str(e)}", exc_info=True)
+            return f"Error exporting data: {str(e)}"
+    
+    def get_recent_queries(self, limit=10):
+        """Get most recent queries for display in the UI"""
+        try:
+            if self.feedback_collection is None:
+                logger.error("MongoDB connection not available. Cannot get recent queries.")
+                return []
+            
+            # Get most recent queries
+            cursor = self.feedback_collection.find(
+                {}, 
+                {"original_query": 1, "hallucination_detected": 1, "timestamp": 1}
+            ).sort("timestamp", pymongo.DESCENDING).limit(limit)
+            
+            # Convert to list of dictionaries
+            recent_queries = []
+            for doc in cursor:
+                recent_queries.append({
+                    "id": str(doc["_id"]),
+                    "query": doc["original_query"],
+                    "hallucination_detected": doc.get("hallucination_detected", False),
+                    "timestamp": doc["timestamp"].strftime("%Y-%m-%d %H:%M:%S") if isinstance(doc["timestamp"], datetime) else doc["timestamp"]
+                })
+            
+            return recent_queries
+        except Exception as e:
+            logger.error(f"Error getting recent queries: {str(e)}", exc_info=True)
+            return []
+    
+    def get_query_details(self, query_id):
+        """Get full details for a specific query by ID"""
+        try:
+            if self.feedback_collection is None:
+                logger.error("MongoDB connection not available. Cannot get query details.")
+                return None
+            
+            # Convert string ID to ObjectId
+            obj_id = ObjectId(query_id)
+            
+            # Find the query by ID
+            doc = self.feedback_collection.find_one({"_id": obj_id})
+            
+            if doc is None:
+                logger.warning(f"No query found with ID {query_id}")
+                return None
+            
+            # Convert ObjectId to string for JSON serialization
+            doc["_id"] = str(doc["_id"])
+            
+            # Convert timestamp to string
+            if "timestamp" in doc and isinstance(doc["timestamp"], datetime):
+                doc["timestamp"] = doc["timestamp"].strftime("%Y-%m-%d %H:%M:%S")
+            
+            return doc
+        except Exception as e:
+            logger.error(f"Error getting query details: {str(e)}", exc_info=True)
+            return None
+
+
+# Progress tracking for UI updates
+class ProgressTracker:
+    """Tracks progress of hallucination detection for UI updates"""
+    
+    STAGES = {
+        "idle": {"status": "Ready", "progress": 0, "color": "#757575"},
+        "starting": {"status": "Starting process...", "progress": 5, "color": "#2196F3"},
+        "generating_paraphrases": {"status": "Generating paraphrases...", "progress": 15, "color": "#2196F3"},
+        "paraphrases_complete": {"status": "Paraphrases generated", "progress": 30, "color": "#2196F3"},
+        "getting_responses": {"status": "Getting responses (0/0)...", "progress": 35, "color": "#2196F3"},
+        "responses_progress": {"status": "Getting responses ({completed}/{total})...", "progress": 40, "color": "#2196F3"},
+        "responses_complete": {"status": "All responses received", "progress": 65, "color": "#2196F3"},
+        "judging": {"status": "Analyzing responses for hallucinations...", "progress": 70, "color": "#2196F3"},
+        "complete": {"status": "Analysis complete!", "progress": 100, "color": "#4CAF50"},
+        "error": {"status": "Error: {error_message}", "progress": 100, "color": "#F44336"}
+    }
+    
+    def __init__(self):
+        self.stage = "idle"
+        self.stage_data = self.STAGES[self.stage].copy()
+        self.query = ""
+        self.completed_responses = 0
+        self.total_responses = 0
+        self.error_message = ""
+        self._lock = threading.Lock()
+        self._status_callback = None
+        self._stop_event = threading.Event()
+        self._update_thread = None
+    
+    def register_callback(self, callback_fn):
+        """Register callback function to update UI"""
+        self._status_callback = callback_fn
+    
+    def update_stage(self, stage, **kwargs):
+        """Update the current stage and trigger callback"""
+        with self._lock:
+            if stage in self.STAGES:
+                self.stage = stage
+                self.stage_data = self.STAGES[stage].copy()
+                
+                # Update with any additional parameters
+                for key, value in kwargs.items():
+                    if key == 'query':
+                        self.query = value
+                    elif key == 'completed_responses':
+                        self.completed_responses = value
+                    elif key == 'total_responses':
+                        self.total_responses = value
+                    elif key == 'error_message':
+                        self.error_message = value
+                
+                # Format status message
+                if stage == 'responses_progress':
+                    self.stage_data['status'] = self.stage_data['status'].format(
+                        completed=self.completed_responses, 
+                        total=self.total_responses
+                    )
+                elif stage == 'error':
+                    self.stage_data['status'] = self.stage_data['status'].format(
+                        error_message=self.error_message
+                    )
+                
+                if self._status_callback:
+                    self._status_callback(self.get_html_status())
+    
+    def get_html_status(self):
+        """Get HTML representation of current status"""
+        progress_width = f"{self.stage_data['progress']}%"
+        status_text = self.stage_data['status']
+        color = self.stage_data['color']
+        
+        query_info = f'<div class="query-display">{self.query}</div>' if self.query else ''
+        
+        # Only show status text if not in idle state
+        status_display = f'<div class="progress-status" style="color: {color};">{status_text}</div>' if self.stage != "idle" else ''
+        
+        html = f"""
+        <div class="progress-container">
+            {query_info}
+            {status_display}
+            <div class="progress-bar-container">
+                <div class="progress-bar" style="width: {progress_width}; background-color: {color};"></div>
+            </div>
+        </div>
+        """
+        return html
+    
+    def start_pulsing(self):
+        """Start a pulsing animation for the progress bar during long operations"""
+        if self._update_thread and self._update_thread.is_alive():
+            return
+        
+        self._stop_event.clear()
+        self._update_thread = threading.Thread(target=self._pulse_progress)
+        self._update_thread.daemon = True
+        self._update_thread.start()
+    
+    def stop_pulsing(self):
+        """Stop the pulsing animation"""
+        self._stop_event.set()
+        if self._update_thread:
+            self._update_thread.join(0.5)
+    
+    def _pulse_progress(self):
+        """Animate the progress bar to show activity"""
+        pulse_stages = ["⋯", "⋯⋯", "⋯⋯⋯", "⋯⋯", "⋯"]
+        i = 0
+        while not self._stop_event.is_set():
+            with self._lock:
+                if self.stage not in ["idle", "complete", "error"]:
+                    status_base = self.stage_data['status'].split("...")[0] if "..." in self.stage_data['status'] else self.stage_data['status']
+                    self.stage_data['status'] = f"{status_base}... {pulse_stages[i]}"
+                    
+                    if self._status_callback:
+                        self._status_callback(self.get_html_status())
+            
+            i = (i + 1) % len(pulse_stages)
+            time.sleep(0.3)
+
+
+def create_interface():
+    """Create Gradio interface"""
+    detector = HallucinationDetectorApp()
+    
+    # Initialize Progress Tracker
+    progress_tracker = ProgressTracker()
+    
+    # Initialize APIs from environment variables automatically
+    try:
+        detector.initialize_api(
+            mistral_api_key=os.environ.get("HF_MISTRAL_API_KEY"),
+            openai_api_key=os.environ.get("HF_OPENAI_API_KEY")
+        )
+    except Exception as e:
+        print(f"Warning: Failed to initialize APIs from environment variables: {e}")
+        print("Please make sure HF_MISTRAL_API_KEY and HF_OPENAI_API_KEY are set in your environment")
+    
+    # CSS for styling
+    css = """
+    .container {
+        max-width: 1000px;
+        margin: 0 auto;
+    }
+    .title {
+        text-align: center;
+        margin-bottom: 0.5em;
+        color: #1a237e;
+        font-weight: 600;
+    }
+    .subtitle {
+        text-align: center;
+        margin-bottom: 1.5em;
+        color: #455a64;
+        font-size: 1.2em;
+    }
+    .section-title {
+        margin-top: 1em;
+        margin-bottom: 0.5em;
+        font-weight: bold;
+        color: #283593;
+    }
+    .info-box {
+        padding: 1.2em;
+        border-radius: 8px;
+        background-color: #f5f5f5;
+        margin-bottom: 1em;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+    }
+    .hallucination-positive {
+        padding: 1.2em;
+        border-radius: 8px;
+        background-color: #ffebee;
+        border-left: 5px solid #f44336;
+        margin-bottom: 1em;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+    }
+    .hallucination-negative {
+        padding: 1.2em;
+        border-radius: 8px;
+        background-color: #e8f5e9;
+        border-left: 5px solid #4caf50;
+        margin-bottom: 1em;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+    }
+    .response-box {
+        padding: 1.2em;
+        border-radius: 8px;
+        background-color: #f5f5f5;
+        margin-bottom: 0.8em;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+    }
+    .example-queries {
+        display: flex;
+        flex-wrap: wrap;
+        gap: 8px;
+        margin-bottom: 15px;
+    }
+    .example-query {
+        background-color: #e3f2fd;
+        padding: 8px 15px;
+        border-radius: 18px;
+        font-size: 0.9em;
+        cursor: pointer;
+        transition: all 0.2s;
+        border: 1px solid #bbdefb;
+    }
+    .example-query:hover {
+        background-color: #bbdefb;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.1);
+    }
+    .stats-section {
+        display: flex;
+        justify-content: space-between;
+        background-color: #e8eaf6;
+        padding: 15px;
+        border-radius: 8px;
+        margin-bottom: 20px;
+    }
+    .stat-item {
+        text-align: center;
+        padding: 10px;
+    }
+    .stat-value {
+        font-size: 1.5em;
+        font-weight: bold;
+        color: #303f9f;
+    }
+    .stat-label {
+        font-size: 0.9em;
+        color: #5c6bc0;
+    }
+    .feedback-section {
+        border-top: 1px solid #e0e0e0;
+        padding-top: 15px;
+        margin-top: 20px;
+    }
+    footer {
+        text-align: center;
+        padding: 20px;
+        margin-top: 30px;
+        color: #9e9e9e;
+        font-size: 0.9em;
+    }
+    .processing-status {
+        padding: 12px;
+        background-color: #fff3e0;
+        border-left: 4px solid #ff9800;
+        margin-bottom: 15px;
+        font-weight: 500;
+        color: #e65100;
+    }
+    .debug-panel {
+        background-color: #f5f5f5;
+        border: 1px solid #e0e0e0;
+        border-radius: 4px;
+        padding: 10px;
+        margin-top: 15px;
+        font-family: monospace;
+        font-size: 0.9em;
+        white-space: pre-wrap;
+        max-height: 200px;
+        overflow-y: auto;
+    }
+    .progress-container {
+        padding: 15px;
+        background-color: #fff;
+        border-radius: 8px;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+        margin-bottom: 15px;
+    }
+    .progress-status {
+        font-weight: 500;
+        margin-bottom: 8px;
+        padding: 4px 0;
+        font-size: 0.95em;
+    }
+    .progress-bar-container {
+        background-color: #e0e0e0;
+        height: 10px;
+        border-radius: 5px;
+        overflow: hidden;
+        margin-bottom: 10px;
+        box-shadow: inset 0 1px 3px rgba(0,0,0,0.1);
+    }
+    .progress-bar {
+        height: 100%;
+        transition: width 0.5s ease;
+        background-image: linear-gradient(to right, #2196F3, #3f51b5);
+    }
+    .query-display {
+        font-style: italic;
+        color: #666;
+        margin-bottom: 10px;
+        background-color: #f5f5f5;
+        padding: 8px;
+        border-radius: 4px;
+        border-left: 3px solid #2196F3;
+    }
+    """
+    
+    # Example queries
+    example_queries = [
+        "Who was the first person to land on the moon?",
+        "What is the capital of France?",
+        "How many planets are in our solar system?",
+        "Who wrote the novel 1984?",
+        "What is the speed of light?",
+        "What was the first computer?"
+    ]
+    
+    # Function to update the progress display
+    def update_progress_display(html):
+        """Update the progress display with the provided HTML"""
+        return gr.update(visible=True, value=html)
+    
+    # Register the callback with the tracker
+    progress_tracker.register_callback(update_progress_display)
+    
+    # Register the tracker with the detector
+    detector.set_progress_callback(progress_tracker.update_stage)
+    
+    # Helper function to set example query
+    def set_example_query(example):
+        return example
+    
+    # Function to show processing is starting
+    def start_processing(query):
+        logger.info("Processing query: %s", query)
+        # Stop any existing pulsing to prepare for incremental progress updates
+        progress_tracker.stop_pulsing()
+        
+        # Reset to a processing state without the "Ready" text
+        # Use "starting" stage but with minimal UI display
+        progress_tracker.stage = "starting"
+        progress_tracker.query = query
+        
+        # Force UI update with clean display
+        if progress_tracker._status_callback:
+            progress_tracker._status_callback(progress_tracker.get_html_status())
+        
+        return [
+            gr.update(visible=True),  # Show the progress display
+            gr.update(visible=False),  # Hide the results accordion
+            gr.update(visible=False),  # Hide the feedback accordion
+            None  # Reset hidden results
+        ]
+    
+    # Main processing function
+    def process_query_and_display_results(query, progress=gr.Progress()):
+        if not query.strip():
+            logger.warning("Empty query submitted")
+            progress_tracker.stop_pulsing()
+            progress_tracker.update_stage("error", error_message="Please enter a query.")
+            return [
+                gr.update(visible=True),  # Show the progress with error
+                gr.update(visible=False),
+                gr.update(visible=False),
+                None
+            ]
+            
+        # Check if API is initialized
+        if not detector.pas2:
+            try:
+                # Try to initialize from environment variables
+                logger.info("Initializing APIs from environment variables")
+                progress(0.05, desc="Initializing API...")
+                init_message = detector.initialize_api(
+                    mistral_api_key=os.environ.get("HF_MISTRAL_API_KEY"),
+                    openai_api_key=os.environ.get("HF_OPENAI_API_KEY")
+                )
+                if "successfully" not in init_message:
+                    logger.error("Failed to initialize APIs: %s", init_message)
+                    progress_tracker.stop_pulsing()
+                    progress_tracker.update_stage("error", error_message="API keys not found in environment variables.")
+                    return [
+                        gr.update(visible=True),
+                        gr.update(visible=False),
+                        gr.update(visible=False),
+                        None
+                    ]
+            except Exception as e:
+                logger.error("Error initializing API: %s", str(e), exc_info=True)
+                progress_tracker.stop_pulsing()
+                progress_tracker.update_stage("error", error_message=f"Error initializing API: {str(e)}")
+                return [
+                    gr.update(visible=True),
+                    gr.update(visible=False),
+                    gr.update(visible=False),
+                    None
+                ]
+        
+        try:
+            # Process the query
+            logger.info("Starting hallucination detection process")
+            start_time = time.time()
+            
+            # Set up a custom progress callback that uses both the progress_tracker and the gr.Progress
+            def combined_progress_callback(stage, **kwargs):
+                # Skip the idle stage, which shows "Ready"
+                if stage == "idle":
+                    return
+                    
+                progress_tracker.update_stage(stage, **kwargs)
+                
+                # Map the stages to progress values for the gr.Progress bar
+                stage_to_progress = {
+                    "starting": 0.05,
+                    "generating_paraphrases": 0.15,
+                    "paraphrases_complete": 0.3,
+                    "getting_responses": 0.35,
+                    "responses_progress": lambda kwargs: 0.35 + (0.3 * (kwargs.get("completed", 0) / max(kwargs.get("total", 1), 1))),
+                    "responses_complete": 0.65,
+                    "judging": 0.7,
+                    "complete": 1.0,
+                    "error": 1.0
+                }
+                
+                # Update the gr.Progress bar
+                if stage in stage_to_progress:
+                    prog_value = stage_to_progress[stage]
+                    if callable(prog_value):
+                        prog_value = prog_value(kwargs)
+                    
+                    desc = progress_tracker.STAGES[stage]["status"]
+                    if "{" in desc and "}" in desc:
+                        # Format the description with any kwargs
+                        desc = desc.format(**kwargs)
+                    
+                    # Ensure UI updates by adding a small delay
+                    # This forces the progress updates to be rendered
+                    progress(prog_value, desc=desc)
+                    
+                    # For certain key stages, add a small sleep to ensure progress is visible
+                    if stage in ["starting", "generating_paraphrases", "paraphrases_complete", 
+                                "getting_responses", "responses_complete", "judging", "complete"]:
+                        time.sleep(0.2)  # Small delay to ensure UI update is visible
+            
+            # Use these steps for processing
+            detector.set_progress_callback(combined_progress_callback)
+            
+            # Create a wrapper function for detect_hallucination that gives more control over progress updates
+            def run_detection_with_visible_progress():
+                # Step 1: Start
+                combined_progress_callback("starting", query=query)
+                time.sleep(0.3)  # Ensure starting status is visible
+                
+                # Step 2: Generate paraphrases (15-30%)
+                combined_progress_callback("generating_paraphrases", query=query)
+                all_queries = detector.pas2.generate_paraphrases(query)
+                combined_progress_callback("paraphrases_complete", query=query, count=len(all_queries))
+                
+                # Step 3: Get responses (35-65%)
+                combined_progress_callback("getting_responses", query=query, total=len(all_queries))
+                all_responses = []
+                for i, q in enumerate(all_queries):
+                    # Show incremental progress for each response
+                    combined_progress_callback("responses_progress", query=query, completed=i, total=len(all_queries))
+                    response = detector.pas2._get_single_response(q, index=i)
+                    all_responses.append(response)
+                combined_progress_callback("responses_complete", query=query)
+                
+                # Step 4: Judge hallucinations (70-100%)
+                combined_progress_callback("judging", query=query)
+                
+                # The first query is the original, rest are paraphrases
+                original_query = all_queries[0]
+                original_response = all_responses[0]
+                paraphrased_queries = all_queries[1:] if len(all_queries) > 1 else []
+                paraphrased_responses = all_responses[1:] if len(all_responses) > 1 else []
+                
+                # Judge the responses
+                judgment = detector.pas2.judge_hallucination(
+                    original_query=original_query,
+                    original_response=original_response,
+                    paraphrased_queries=paraphrased_queries,
+                    paraphrased_responses=paraphrased_responses
+                )
+                
+                # Assemble the results
+                results = {
+                    "original_query": original_query,
+                    "original_response": original_response,
+                    "paraphrased_queries": paraphrased_queries,
+                    "paraphrased_responses": paraphrased_responses,
+                    "hallucination_detected": judgment.hallucination_detected,
+                    "confidence_score": judgment.confidence_score,
+                    "conflicting_facts": judgment.conflicting_facts,
+                    "reasoning": judgment.reasoning,
+                    "summary": judgment.summary
+                }
+                
+                # Show completion
+                combined_progress_callback("complete", query=query)
+                time.sleep(0.3)  # Ensure complete status is visible
+                
+                return results
+            
+            # Run the detection process with visible progress
+            results = run_detection_with_visible_progress()
+            
+            # Calculate elapsed time
+            elapsed_time = time.time() - start_time
+            logger.info("Hallucination detection completed in %.2f seconds", elapsed_time)
+            
+            # Check for errors
+            if "error" in results:
+                logger.error("Error in results: %s", results["error"])
+                progress_tracker.stop_pulsing()
+                progress_tracker.update_stage("error", error_message=results["error"])
+                return [
+                    gr.update(visible=True),
+                    gr.update(visible=False),
+                    gr.update(visible=False),
+                    None
+                ]
+            
+            # Prepare responses for display
+            original_query = results["original_query"]
+            original_response = results["original_response"]
+            
+            paraphrased_queries = results["paraphrased_queries"]
+            paraphrased_responses = results["paraphrased_responses"]
+            
+            hallucination_detected = results["hallucination_detected"]
+            confidence = results["confidence_score"]
+            reasoning = results["reasoning"]
+            summary = results["summary"]
+            
+            # Format conflicting facts
+            conflicting_facts = results["conflicting_facts"]
+            conflicting_facts_text = ""
+            if conflicting_facts:
+                for i, fact in enumerate(conflicting_facts, 1):
+                    conflicting_facts_text += f"{i}. "
+                    if isinstance(fact, dict):
+                        for key, value in fact.items():
+                            conflicting_facts_text += f"{key}: {value}, "
+                        conflicting_facts_text = conflicting_facts_text.rstrip(", ")
+                    else:
+                        conflicting_facts_text += str(fact)
+                    conflicting_facts_text += "\n"
+            
+            # Format responses to escape any backslashes
+            original_response_safe = original_response.replace('\\', '\\\\').replace('\n', '<br>')
+            paraphrased_responses_safe = [r.replace('\\', '\\\\').replace('\n', '<br>') for r in paraphrased_responses]
+            reasoning_safe = reasoning.replace('\\', '\\\\').replace('\n', '<br>')
+            conflicting_facts_text_safe = conflicting_facts_text.replace('\\', '\\\\').replace('\n', '<br>') if conflicting_facts_text else "None identified"
+
+            html_output = f"""
+            <div class="container">
+                <h2 class="title">Hallucination Detection Results</h2>
+                
+                <div class="stats-section">
+                    <div class="stat-item">
+                        <div class="stat-value">{'Yes' if hallucination_detected else 'No'}</div>
+                        <div class="stat-label">Hallucination Detected</div>
+                    </div>
+                    <div class="stat-item">
+                        <div class="stat-value">{confidence:.2f}</div>
+                        <div class="stat-label">Confidence Score</div>
+                    </div>
+                    <div class="stat-item">
+                        <div class="stat-value">{len(paraphrased_queries)}</div>
+                        <div class="stat-label">Paraphrases Analyzed</div>
+                    </div>
+                    <div class="stat-item">
+                        <div class="stat-value">{elapsed_time:.1f}s</div>
+                        <div class="stat-label">Processing Time</div>
+                    </div>
+                </div>
+                
+                <div class="{'hallucination-positive' if hallucination_detected else 'hallucination-negative'}">
+                    <h3>Analysis Summary</h3>
+                    <p>{summary}</p>
+                </div>
+                
+                <div class="section-title">Original Query</div>
+                <div class="response-box">
+                    {original_query}
+                </div>
+                
+                <div class="section-title">Original Response</div>
+                <div class="response-box">
+                    {original_response_safe}
+                </div>
+                
+                <div class="section-title">Paraphrased Queries and Responses</div>
+            """
+            
+            for i, (q, r) in enumerate(zip(paraphrased_queries, paraphrased_responses_safe), 1):
+                html_output += f"""
+                <div class="section-title">Paraphrased Query {i}</div>
+                <div class="response-box">
+                    {q}
+                </div>
+                
+                <div class="section-title">Response {i}</div>
+                <div class="response-box">
+                    {r}
+                </div>
+                """
+            
+            html_output += f"""
+                <div class="section-title">Detailed Analysis</div>
+                <div class="info-box">
+                    <p><strong>Reasoning:</strong></p>
+                    <p>{reasoning_safe}</p>
+                    
+                    <p><strong>Conflicting Facts:</strong></p>
+                    <p>{conflicting_facts_text_safe}</p>
+                </div>
+            </div>
+            """
+            
+            logger.info("Updating UI with results")
+            progress_tracker.stop_pulsing()
+            
+            return [
+                gr.update(visible=False),  # Hide progress display when showing results
+                gr.update(visible=True, value=html_output),
+                gr.update(visible=True),
+                results
+            ]
+            
+        except Exception as e:
+            logger.error("Error processing query: %s", str(e), exc_info=True)
+            progress_tracker.stop_pulsing()
+            progress_tracker.update_stage("error", error_message=f"Error processing query: {str(e)}")
+            return [
+                gr.update(visible=True),
+                gr.update(visible=False),
+                gr.update(visible=False),
+                None
+            ]
+    
+    # Helper function to submit feedback and update stats
+    def combine_feedback(fb_input, fb_text, results):
+        combined_feedback = f"{fb_input}: {fb_text}" if fb_text else fb_input
+        if not results:
+            return "No results to attach feedback to.", ""
+        
+        response = detector.save_feedback(results, combined_feedback)
+        
+        # Get updated stats
+        stats = detector.get_feedback_stats()
+        if stats:
+            stats_html = f"""
+            <div class="stats-section" style="margin-top: 15px;">
+                <div class="stat-item">
+                    <div class="stat-value">{stats['total_feedback']}</div>
+                    <div class="stat-label">Total Feedback</div>
+                </div>
+                <div class="stat-item">
+                    <div class="stat-value">{stats['hallucinations_detected']}</div>
+                    <div class="stat-label">Hallucinations Found</div>
+                </div>
+                <div class="stat-item">
+                    <div class="stat-value">{stats['no_hallucinations']}</div>
+                    <div class="stat-label">No Hallucinations</div>
+                </div>
+                <div class="stat-item">
+                    <div class="stat-value">{stats['average_confidence']}</div>
+                    <div class="stat-label">Avg. Confidence</div>
+                </div>
+            </div>
+            """
+        else:
+            stats_html = ""
+        
+        return response, stats_html
+    
+    # Create the interface
+    with gr.Blocks(css=css, theme=gr.themes.Soft()) as interface:
+        gr.HTML(
+            """
+            <div style="text-align: center; margin-bottom: 1.5rem">
+                <h1 style="font-size: 2.2em; font-weight: 600; color: #1a237e; margin-bottom: 0.2em;">PAS2 - Hallucination Detector</h1>
+                <h3 style="font-size: 1.3em; color: #455a64; margin-bottom: 0.8em;">Advanced AI Response Verification Using Model-as-Judge</h3>
+                <p style="font-size: 1.1em; color: #546e7a; max-width: 800px; margin: 0 auto;">
+                    This tool detects hallucinations in AI responses by comparing answers to semantically equivalent questions and using a specialized judge model.
+                </p>
+            </div>
+            """
+        )
+        
+        with gr.Accordion("About this Tool", open=False):
+            gr.Markdown(
+                """
+                ### How It Works
+                
+                This tool implements the Paraphrase-based Approach for Scrutinizing Systems (PAS2) with a model-as-judge enhancement:
+                
+                1. **Paraphrase Generation**: Your question is paraphrased multiple ways while preserving its core meaning
+                2. **Multiple Responses**: All questions (original + paraphrases) are sent to Mistral Large model
+                3. **Expert Judgment**: OpenAI's o3-mini analyzes all responses to detect factual inconsistencies
+                
+                ### Why This Approach?
+                
+                When an AI hallucinates, it often provides different answers to the same question when phrased differently. 
+                By using a separate judge model, we can identify these inconsistencies more effectively than with 
+                metric-based approaches.
+                
+                ### Understanding the Results
+                
+                - **Confidence Score**: Indicates the judge's confidence in the hallucination detection
+                - **Conflicting Facts**: Specific inconsistencies found across responses
+                - **Reasoning**: The judge's detailed analysis explaining its decision
+                
+                ### Privacy Notice
+                
+                Your queries and the system's responses are saved to help improve hallucination detection.
+                No personally identifiable information is collected.
+                """
+            )
+        
+        with gr.Row():
+            with gr.Column():
+                # First define the query input
+                gr.Markdown("### Enter Your Question")
+                with gr.Row():
+                    query_input = gr.Textbox(
+                        label="",
+                        placeholder="Ask a factual question (e.g., Who was the first person to land on the moon?)",
+                        lines=3
+                    )
+                
+                # Now define the example queries
+                gr.Markdown("### Or Try an Example")
+                example_row = gr.Row()
+                with example_row:
+                    for example in example_queries:
+                        example_btn = gr.Button(
+                            example, 
+                            elem_classes=["example-query"],
+                            scale=0
+                        )
+                        example_btn.click(
+                            fn=set_example_query,
+                            inputs=[gr.Textbox(value=example, visible=False)],
+                            outputs=[query_input]
+                        )
+                
+                with gr.Row():
+                    submit_button = gr.Button("Detect Hallucinations", variant="primary", scale=1)
+        
+        # Error message
+        error_message = gr.HTML(
+            label="Status",
+            visible=False
+        )
+        
+        # Progress display
+        progress_display = gr.HTML(
+            value=progress_tracker.get_html_status(),
+            visible=True
+        )
+        
+        # Results display
+        results_accordion = gr.HTML(visible=False)
+        
+        # Add feedback stats display
+        feedback_stats = gr.HTML(visible=True)
+        
+        # Feedback section
+        with gr.Accordion("Provide Feedback", open=False, visible=False) as feedback_accordion:
+            gr.Markdown("### Help Improve the System")
+            gr.Markdown("Your feedback helps us refine the hallucination detection system.")
+            
+            feedback_input = gr.Radio(
+                label="Is the hallucination detection accurate?",
+                choices=["Yes, correct detection", "No, incorrectly flagged hallucination", "No, missed hallucination", "Unsure/Other"],
+                value="Yes, correct detection"
+            )
+            
+            feedback_text = gr.Textbox(
+                label="Additional comments (optional)",
+                placeholder="Please provide any additional observations or details...",
+                lines=2
+            )
+            
+            feedback_button = gr.Button("Submit Feedback", variant="secondary")
+            feedback_status = gr.Textbox(label="Feedback Status", interactive=False, visible=False)
+            
+            # Initialize feedback stats
+            initial_stats = detector.get_feedback_stats()
+            if initial_stats:
+                feedback_stats.value = f"""
+                <div class="stats-section">
+                    <div class="stat-item">
+                        <div class="stat-value">{initial_stats['total_feedback']}</div>
+                        <div class="stat-label">Total Feedback</div>
+                    </div>
+                    <div class="stat-item">
+                        <div class="stat-value">{initial_stats['hallucinations_detected']}</div>
+                        <div class="stat-label">Hallucinations Found</div>
+                    </div>
+                    <div class="stat-item">
+                        <div class="stat-value">{initial_stats['no_hallucinations']}</div>
+                        <div class="stat-label">No Hallucinations</div>
+                    </div>
+                    <div class="stat-item">
+                        <div class="stat-value">{initial_stats['average_confidence']}</div>
+                        <div class="stat-label">Avg. Confidence</div>
+                    </div>
+                </div>
+                """
+        
+        # Hidden state to store results for feedback
+        hidden_results = gr.State()
+        
+        # Set up event handlers
+        submit_button.click(
+            fn=start_processing,
+            inputs=[query_input],
+            outputs=[progress_display, results_accordion, feedback_accordion, hidden_results],
+            queue=False
+        ).then(
+            fn=process_query_and_display_results,
+            inputs=[query_input],
+            outputs=[progress_display, results_accordion, feedback_accordion, hidden_results]
+        )
+        
+        feedback_button.click(
+            fn=combine_feedback,
+            inputs=[feedback_input, feedback_text, hidden_results],
+            outputs=[feedback_status, feedback_stats]
+        )
+        
+        # Footer
+        gr.HTML(
+            """
+            <footer>
+                <p>Paraphrase-based Approach for Scrutinizing Systems (PAS2) - Advanced Hallucination Detection</p>
+                <p>Using Mistral Large for generation and OpenAI o3-mini as judge</p>
+            </footer>
+            """
+        )
+    
+    return interface
+
+# Add a test function to demonstrate progress bar in isolation
+def test_progress():
+    """Simple test function to demonstrate progress bar"""
+    import gradio as gr
+    import time
+    
+    def slow_process(progress=gr.Progress()):
+        progress(0, desc="Starting process...")
+        time.sleep(0.5)
+        
+        # Phase 1: Generating paraphrases
+        progress(0.15, desc="Generating paraphrases...")
+        time.sleep(1)
+        progress(0.3, desc="Paraphrases generated")
+        time.sleep(0.5)
+        
+        # Phase 2: Getting responses
+        progress(0.35, desc="Getting responses...")
+        # Show incremental progress for responses
+        for i in range(3):
+            time.sleep(0.8)
+            prog = 0.35 + (0.3 * ((i+1) / 3))
+            progress(prog, desc=f"Getting responses ({i+1}/3)...")
+        
+        progress(0.65, desc="All responses received")
+        time.sleep(0.5)
+        
+        # Phase 3: Analyzing
+        progress(0.7, desc="Analyzing responses for hallucinations...")
+        time.sleep(2)
+        
+        # Complete
+        progress(1.0, desc="Analysis complete!")
+        return "Process completed successfully!"
+    
+    with gr.Blocks() as demo:
+        with gr.Row():
+            btn = gr.Button("Start Process")
+            output = gr.Textbox(label="Result")
+        
+        btn.click(fn=slow_process, outputs=output)
+    
+    demo.launch()
+
+# Main application entry point
+if __name__ == "__main__":
+    logger.info("Starting PAS2 Hallucination Detector")
+    interface = create_interface()
+    logger.info("Launching Gradio interface...")
+    interface.launch(
+        server_name="0.0.0.0",  # Bind to all interfaces
+        server_port=7860,       # Default Hugging Face Spaces port
+        show_api=False, 
+        quiet=True,  # Changed to True for Hugging Face deployment
+        share=False,
+        max_threads=10,
+        debug=False  # Changed to False for production deployment
+    )
+    
+# Uncomment this line to run the test function instead of the main interface
+# if __name__ == "__main__":
+#     test_progress()

pas2_fork/migrate_db.py

@@ -0,0 +1,139 @@
+#!/usr/bin/env python
+"""
+Migration script to move data from SQLite to MongoDB.
+Run this once to migrate existing data to your new MongoDB database.
+"""
+
+import os
+import sqlite3
+import json
+from datetime import datetime
+from pymongo import MongoClient
+from dotenv import load_dotenv
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s [%(levelname)s] %(message)s',
+    handlers=[
+        logging.StreamHandler()
+    ]
+)
+
+logger = logging.getLogger(__name__)
+
+def migrate_sqlite_to_mongodb():
+    """Migrate data from SQLite to MongoDB"""
+    
+    # Load environment variables
+    load_dotenv()
+    
+    # Get MongoDB connection string from environment variable
+    mongo_uri = os.environ.get("MONGODB_URI")
+    
+    if not mongo_uri:
+        logger.error("MONGODB_URI not found in environment variables. Please set it before running this script.")
+        return False
+    
+    try:
+        # Connect to MongoDB
+        logger.info("Connecting to MongoDB...")
+        mongo_client = MongoClient(mongo_uri)
+        
+        # Access database and collection
+        db = mongo_client["hallucination_detector"]
+        feedback_collection = db["feedback"]
+        
+        # Check for existing data
+        existing_count = feedback_collection.count_documents({})
+        logger.info(f"MongoDB already contains {existing_count} documents")
+        
+        # Determine SQLite database path
+        data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
+        db_path = os.path.join(data_dir, "feedback.db")
+        
+        if not os.path.exists(db_path):
+            logger.warning(f"SQLite database not found at {db_path}. No data to migrate.")
+            return True
+        
+        # Connect to SQLite
+        logger.info(f"Connecting to SQLite database at {db_path}...")
+        conn = sqlite3.connect(db_path)
+        conn.row_factory = sqlite3.Row  # This enables column access by name
+        cursor = conn.cursor()
+        
+        # Get all records
+        cursor.execute("SELECT * FROM feedback")
+        rows = cursor.fetchall()
+        
+        if not rows:
+            logger.info("No data found in SQLite database.")
+            conn.close()
+            return True
+        
+        logger.info(f"Found {len(rows)} records in SQLite database")
+        
+        # Process rows and insert into MongoDB
+        mongo_docs = []
+        for row in rows:
+            # Convert row to dict
+            row_dict = dict(row)
+            
+            # Parse special fields
+            try:
+                row_dict["paraphrased_queries"] = json.loads(row_dict["paraphrased_queries"])
+            except:
+                row_dict["paraphrased_queries"] = []
+                
+            try:
+                row_dict["paraphrased_responses"] = json.loads(row_dict["paraphrased_responses"])
+            except:
+                row_dict["paraphrased_responses"] = []
+                
+            try:
+                row_dict["conflicting_facts"] = json.loads(row_dict["conflicting_facts"])
+            except:
+                row_dict["conflicting_facts"] = []
+            
+            # Convert integer to boolean
+            row_dict["hallucination_detected"] = bool(row_dict["hallucination_detected"])
+            
+            # Parse timestamp
+            try:
+                row_dict["timestamp"] = datetime.strptime(row_dict["timestamp"], "%Y-%m-%d %H:%M:%S")
+            except:
+                row_dict["timestamp"] = datetime.now()
+            
+            # Remove sqlite id
+            if "id" in row_dict:
+                del row_dict["id"]
+            
+            mongo_docs.append(row_dict)
+        
+        # Insert all documents
+        if mongo_docs:
+            logger.info(f"Inserting {len(mongo_docs)} documents into MongoDB...")
+            result = feedback_collection.insert_many(mongo_docs)
+            logger.info(f"Successfully migrated {len(result.inserted_ids)} records to MongoDB")
+        
+        # Close SQLite connection
+        conn.close()
+        
+        # Verify data in MongoDB
+        new_count = feedback_collection.count_documents({})
+        logger.info(f"MongoDB now contains {new_count} documents")
+        
+        return True
+        
+    except Exception as e:
+        logger.error(f"Error during migration: {str(e)}", exc_info=True)
+        return False
+
+if __name__ == "__main__":
+    logger.info("Starting migration from SQLite to MongoDB")
+    success = migrate_sqlite_to_mongodb()
+    if success:
+        logger.info("Migration completed successfully")
+    else:
+        logger.error("Migration failed")

pas2_fork/requirements.txt

@@ -0,0 +1,9 @@
+gradio
+pandas
+numpy
+mistralai
+openai
+pydantic
+python-dotenv
+pymongo
+dnspython