improve interface

app.py

@@ -3,25 +3,47 @@ from lettucedetect.models.inference import HallucinationDetector
 import os
 
 
-title = """# 🙋🏻‍♂️Welcome to 🌟Tonic's 🥬 LettuceDetect - 🤯🧠 Hallucination Tester 🟢🔴"""
-description= """
+title = """# 🙋🏻‍♂️Welcome to 🌟Tonic's 🥬 LettuceDetect - 🤯🧠 Hallucination Tester 🟢🔴
         Powered by `lettucedect-large-modernbert-en-v1` from KRLabsOrg. Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
-        
-        ### How to Use:
-        1. Enter a **Context** (source document or info).
-        2. Enter a **Question** related to the context.
-        3. Enter an **Answer** to evaluate.
-        4. Press **Submit** to see if the answer hallucinates!
-        
-        - 🟢 = No hallucinations
-        - 🔴 = Hallucinations detected
-        - Highlighted text shows hallucinated spans in **red** with confidence scores.
+"""
+description2 = """
+### Model Details
+- **Model Name**: [lettucedect-large-modernbert-en-v1](https://huggingface.co/KRLabsOrg/lettucedect-large-modernbert-en-v1)  
+- **Organization**: [KRLabsOrg](https://huggingface.co/KRLabsOrg)  
+- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)  
+- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens  
+- **Task**: Token Classification / Hallucination Detection  
+- **Training Dataset**: [RagTruth](https://huggingface.co/datasets/wandb/RAGTruth-processed)  
+- **Language**: English  
+- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.  
+LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
+"""
+
+howto1 = """
+### How to Use LettuceDetect Tester
+1. **Enter a Context**: Provide the source text or document (e.g., "France is a country in Europe..."). This is the factual basis for evaluation.
+2. **Enter a Question**: Ask something related to the context (e.g., "What is the capital of France?").
+3. **Enter an Answer**: Input the response you want to check (e.g., "The capital of France is Paris. The population is 69 million.").
+4. **Press Submit**: Analyze the answer for hallucinations!
+"""
+
+howto2 = """
+### Understanding the Output
+- **Status**:  
+  - 🟢 = No hallucinations detected  
+  - 🔴 = Hallucinations detected  
+  - ⚪ = Error occurred  
+- **Explanation**: A brief summary of the result.  
+- **Highlighted Answer**: Shows the answer with hallucinated parts in **red**, labeled with confidence scores (e.g., "hallucination (conf: 0.9944)").  
+- **Hallucinated Spans & Confidence**: Lists each hallucinated segment with its confidence score.  
+- **Average Confidence**: Displays the average confidence of all detected hallucinations (e.g., "Average Confidence: 0.9944").  
+Use this tool to ensure your answers are grounded in reality!
 """
 join_us = """
 ## Join us:
 
 🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 
-[![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/n8ytYeh25n) 
+[Join us on Discord](https://discord.gg/n8ytYeh25n) 
 On 🤗Huggingface: [MultiTransformer](https://huggingface.co/MultiTransformer) 
 On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Data Tonic](https://github.com/multiTonic/thinking-dataset/)
 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
@@ -100,7 +122,12 @@ with gr.Blocks(
     gr.Markdown(title)
     with gr.Row():
         with gr.Group():
-            gr.Markdown(description)
+            gr.Markdown(description2)
+        with gr.Column():
+            gr.Markdown(howto2)
+    with gr.Row():
+        with gr.Group():
+            gr.Markdown(howto1)
         with gr.Column():
             gr.Markdown(join_us)
     with gr.Row():