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import os
import re
import json
import getpass
import logging
import openai
import asyncio
from typing import Any, List, Tuple, Dict
import gradio as gr
import llama_index
from llama_index import Document
from llama_index.llms import OpenAI
from llama_index.embeddings import OpenAIEmbedding, HuggingFaceEmbedding
from llama_index.llms import HuggingFaceLLM
import requests
from RAG_utils import PDFProcessor_Unstructured, PDFQueryEngine, HybridRetriever, MixtralLLM, KeywordSearch, base_utils, ConfigManager
# Configure basic logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# Create a logger object
logger = logging.getLogger(__name__)
os.environ["TOKENIZERS_PARALLELISM"] = "false"
config_manager = ConfigManager()
#config_manager.load_config("api", "Config/api_config.json")
config_manager.load_config("model", "model_config.json")
openai.api_key = os.environ['OPENAI_API_KEY'] #config_manager.get_config_value("api", "OPENAI_API_KEY")
hf_token = os.environ['HF_TOKEN']#config_manager.get_config_value("api", "HF_TOKEN")
# PDF rendering and chunking parameters
pdf_processing_config = config_manager.get_config_value("model", "pdf_processing")
ALLOWED_EXTENSIONS = config_manager.get_config_value("model", "allowed_extensions")
embed = config_manager.get_config_value("model", "embeddings")
embed_model_name = config_manager.get_config_value("model", "embeddings_model")
#llm_model = config_manager.get_config_value("model", "llm_model")
model_temperature = config_manager.get_config_value("model", "model_temp")
output_token_size = config_manager.get_config_value("model", "max_tokens")
model_context_window = config_manager.get_config_value("model", "context_window")
gpt_prompt_path = config_manager.get_config_value("model","GPT_PROMPT_PATH")
mistral_prompt_path = config_manager.get_config_value("model","MISTRAL_PROMPT_PATH")
info_prompt_path = config_manager.get_config_value("model", "INFO_PROMPT_PATH")
peer_review_journals_path = config_manager.get_config_value("model", "peer_review_journals_path")
eq_network_journals_path = config_manager.get_config_value("model", "eq_network_journals_path")
queries = config_manager.get_config_value("model", "queries")
criteria = config_manager.get_config_value("model", "criteria")
num_criteria = len(queries)
author_query = config_manager.get_config_value("model", "author_query")
journal_query = config_manager.get_config_value("model", "journal_query")
title_query = config_manager.get_config_value("model", "title_query")
# Helper function to check if the file extension is allowed
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
def generate_score_bar(score, num_criteria):
# Convert and round the score from a 9-point scale to a 100-point scale
score_out_of_100 = round((score / num_criteria) * 100)
# Determine the color and text based on the original score
if score == 9:
color = "#4CAF50" # green
text = "Very good"
elif score in [7, 8]:
color = "#FFEB3B" # yellow
text = "Good"
elif score in [5, 6]:
color = "#FF9800" # orange
text = "Ok"
elif score in [3, 4]:
color = "#F44336" # red
text = "Bad"
else: # score < 3
color = "#800000" # maroon
text = "Very bad"
# Create the HTML for the score bar
score_bar_html = f"""
<div style="background-color: #ddd; border-radius: 10px; position: relative; height: 20px; width: 100%;">
<div style="background-color: {color}; height: 100%; border-radius: 10px; width: {score_out_of_100}%;"></div>
</div>
<p style="color: {color};">{text}</p> <!-- Display the text -->
"""
return score_bar_html
def format_example(example):
"""
Formats a few-shot example into a string.
Args:
example (dict): A dictionary containing 'query', 'score', and 'reasoning' for the few-shot example.
Returns:
str: Formatted few-shot example text.
"""
return "Example:\nQuery: {}\n Direct Answer: {}\n".format(
example['query'], example['Answer'])
def process_pdf(uploaded_file, llm_model, n_criteria = num_criteria):
# Process the PDF file
pdf_processor = PDFProcessor_Unstructured(pdf_processing_config)
merged_chunks, tables = pdf_processor.process_pdf_file(uploaded_file)
documents = [Document(text=t) for t in merged_chunks]
# Prompts and Queries
utils = base_utils()
info_prompt = utils.read_from_file(info_prompt_path)
# LLM Model choice
try:
if llm_model == "Model 1":
llm = OpenAI(model="gpt-4-1106-preview", temperature=model_temperature, max_tokens=output_token_size)
general_prompt = utils.read_from_file(gpt_prompt_path)
elif llm_model == "Model 2":
if any(param is None for param in [model_context_window, output_token_size, model_temperature, hf_token]):
raise ValueError("All parameters are required for Mistral LLM.")
llm = MixtralLLM(context_window=model_context_window, num_output=output_token_size,
temperature=model_temperature, model_name="mistralai/Mixtral-8x7B-Instruct-v0.1", api_key=hf_token)
general_prompt = utils.read_from_file(mistral_prompt_path)
else:
raise ValueError(f"Unsupported language model: {llm_model}")
except Exception as e:
logger.error(f"Error initializing language model '{llm_model}': {e}", exc_info=True)
raise # Or handle the exception as needed
# Embedding model choice for RAG
try:
if embed == "openai":
embed_model = OpenAIEmbedding(model="text-embedding-3-large")
elif embed == "huggingface":
# Use the specified model name
embed_model = HuggingFaceEmbedding(embed_model_name)
else:
raise ValueError(f"Unsupported embedding model: {embed_model}")
except Exception as e:
logger.error(f"Error initializing embedding model: {e}", exc_info=True)
raise
peer_review_journals = utils.read_from_file(peer_review_journals_path)
eq_network_journals = utils.read_from_file(eq_network_journals_path)
peer_review_journals_list = peer_review_journals.split('\n')
eq_network_journals_list = eq_network_journals.split('\n')
modified_journal_query = "Is the given research paper published in any of the following journals: " + ", ".join(peer_review_journals_list) + "?"
formatted_journal_example = format_example(example_journal)
formatted_author_example = format_example(example_author)
qa_author_prompt_with_example = info_prompt.replace("{few_shot_examples}", formatted_author_example)
qa_journal_prompt_with_example = info_prompt.replace("{few_shot_examples}", formatted_journal_example)
info_llm = OpenAI(model="gpt-4-1106-preview", temperature=model_temperature, max_tokens=100)
pdf_info_query = PDFQueryEngine(documents, info_llm, embed_model, (info_prompt))
info_query_engine = pdf_info_query.setup_query_engine()
journal_result = info_query_engine.query(modified_journal_query).response
author_result = info_query_engine.query(author_query).response
title_result = info_query_engine.query(title_query).response
pdf_criteria_query = PDFQueryEngine(documents, llm, embed_model, (general_prompt))
# Check for prior registration
nlp_methods = KeywordSearch(merged_chunks)
eq_journal_result = nlp_methods.find_journal_name(journal_result, eq_network_journals_list)
peer_journal_result = nlp_methods.find_journal_name(journal_result, peer_review_journals_list)
registration_result = nlp_methods.check_registration()
# Evaluate with OpenAI model
total_score, criteria_met, score_percentage, reasoning = pdf_criteria_query.evaluate_with_llm(registration_result, peer_journal_result, eq_journal_result, queries)
reasoning_html = "<ul>"
for query, reason in zip(criteria, reasoning):
reasoning_html += f"<li style='font-size: 18px;'><strong style='color: forestgreen;'>{query}</strong> <br> Reasoning: {reason}</li>"
reasoning_html += "</ul>"
# Generate the score bar HTML
score_bar_html = generate_score_bar(total_score, n_criteria)
# Return the score as a string and the reasoning as HTML
return str(round((total_score / n_criteria) * 100)) + "/100", score_bar_html, reasoning_html, author_result, title_result
with gr.Blocks(theme=gr.themes.Glass(
text_size="sm",
font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"],
primary_hue="neutral",
secondary_hue="gray")) as demo:
gr.Markdown("## Med Library")
with gr.Row():
file_upload = gr.File(label="Choose a paper", file_types=['.pdf'])
with gr.Row():
models = ["Model 1", "Model 2"]
model_choice = gr.Dropdown(models, label="Choose a model", value="Model 1")
submit_button = gr.Button("Evaluate")
score_output = gr.Textbox(label="Final Score:", interactive=False)
score_bar_output = gr.HTML()
reasoning_output = gr.HTML()
# Heading for Author Information
gr.Markdown("## Title of the paper")
# Output for dynamically generated author information
title_info_output = gr.Markdown()
# Heading for Author Information
gr.Markdown("## Author Information")
# Output for dynamically generated author information
author_info_output = gr.Markdown()
# Set the click event for the button
submit_button.click(
fn=process_pdf,
inputs=[file_upload, model_choice],
outputs=[score_output, score_bar_output, reasoning_output, author_info_output, title_info_output]
)
#Launch the app
demo.launch(share=True, server_name="0.0.0.0", server_port=7860)
# Main route for file upload and display results
# @app.route('/', methods=['GET', 'POST'])
# def upload_and_display_results():
# total_score = 0
# score_percentage = 0
# reasoning = []
# criteria_met = 0
# if request.method == 'POST':
# # Check if the post request has the file part
# if 'file' not in request.files:
# flash('No file part')
# return redirect(request.url)
# file = request.files['file']
# # If user does not select file, browser also submits an empty part without filename
# if file.filename == '':
# flash('No selected file')
# return redirect(request.url)
# if file and allowed_file(file.filename):
# try:
# # Process the PDF file
# pdf_processor = PDFProcessor_Unstructured(pdf_processing_config)
# merged_chunks, tables = pdf_processor.process_pdf_file(file)
# documents = [Document(text=t) for t in merged_chunks]
# # LLM Model choice
# try:
# if llm_model == "gpt-4" or llm_model == "gpt-3.5-turbo":
# llm = OpenAI(model=llm_model, temperature=model_temperature, max_tokens=output_token_size)
# elif llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
# if any(param is None for param in [model_context_window, output_token_size, model_temperature, hf_token]):
# raise ValueError("All parameters are required for Mistral LLM.")
# llm = MixtralLLM(context_window=model_context_window, num_output=output_token_size,
# temperature=model_temperature, model_name=llm_model, api_key=hf_token)
# else:
# raise ValueError(f"Unsupported language model: {llm_model}")
# except Exception as e:
# logger.error(f"Error initializing language model '{llm_model}': {e}", exc_info=True)
# raise # Or handle the exception as needed
# # Embedding model choice for RAG
# try:
# if embed == "openai":
# embed_model = OpenAIEmbedding()
# elif embed == "huggingface":
# if embed_model_name is None:
# # Set to default model if name not provided
# embed_model_name = "BAAI/bge-small-en-v1.5"
# embed_model = HuggingFaceEmbedding(embed_model_name)
# else:
# # Use the specified model name
# embed_model = HuggingFaceEmbedding(embed_model_name)
# else:
# raise ValueError(f"Unsupported embedding model: {embed_model}")
# except Exception as e:
# logger.error(f"Error initializing embedding model: {e}", exc_info=True)
# raise
# # Prompts and Queries
# utils = base_utils()
# general_prompt = utils.read_from_file(general_prompt_path)
# info_prompt = utils.read_from_file(info_prompt_path)
# peer_review_journals = utils.read_from_file(peer_review_journals_path)
# eq_network_journals = utils.read_from_file(eq_network_journals_path)
# peer_review_journals_list = peer_review_journals.split('\n')
# eq_network_journals_list = eq_network_journals.split('\n')
# modified_journal_query = "Is the given research paper published in any of the following journals: " + ", ".join(peer_review_journals_list) + "?"
# pdf_info_query = PDFQueryEngine(documents, llm, embed_model, (info_prompt))
# info_query_engine = pdf_info_query.setup_query_engine()
# journal_result = info_query_engine.query(modified_journal_query).response
# pdf_criteria_query = PDFQueryEngine(documents, llm, embed_model, (general_prompt))
# # Check for prior registration
# nlp_methods = KeywordSearch(merged_chunks)
# eq_journal_result = nlp_methods.find_journal_name(journal_result, eq_network_journals_list)
# peer_journal_result = nlp_methods.find_journal_name(journal_result, peer_review_journals_list)
# registration_result = nlp_methods.check_registration()
# # Evaluate with OpenAI model
# total_score, criteria_met, score_percentage, reasoning = pdf_criteria_query.evaluate_with_llm(registration_result, peer_journal_result, eq_journal_result, queries)
# except Exception as e:
# logging.exception("An error occurred while processing the file.")
# # Consider adding a user-friendly message or redirect
# flash('An error occurred while processing the file.')
# return redirect(request.url)
# return render_template('index.html',
# total_score = total_score,
# score_percentage = score_percentage,
# criteria_met = criteria_met,
# reasoning = reasoning)
# if __name__ == '__main__':
# app.run(debug=True)
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