from llama_index.indices.managed.vectara import VectaraIndex from dotenv import load_dotenv import os from docx import Document from llama_index.llms.together import TogetherLLM from llama_index.core.llms import ChatMessage, MessageRole from Bio import Entrez import ssl from transformers import AutoModelForSequenceClassification, AutoTokenizer import streamlit as st from googleapiclient.discovery import build from typing import List, Optional load_dotenv() os.environ["VECTARA_INDEX_API_KEY"] = os.getenv("VECTARA_INDEX_API_KEY", "zwt_ni_bLu6MRQXzWKPIU__Uubvy_0Xz_FEr-2sfUg") os.environ["VECTARA_QUERY_API_KEY"] = os.getenv("VECTARA_QUERY_API_KEY", "zwt_ni_bLu6MRQXzWKPIU__Uubvy_0Xz_FEr-2sfUg") os.environ["VECTARA_API_KEY"] = os.getenv("VECTARA_API_KEY", "zut_ni_bLoa0I3AeNSjxeZ-UfECnm_9Xv5d4RVBAqw") os.environ["VECTARA_CORPUS_ID"] = os.getenv("VECTARA_CORPUS_ID", "2") os.environ["VECTARA_CUSTOMER_ID"] = os.getenv("VECTARA_CUSTOMER_ID", "2653936430") os.environ["TOGETHER_API"] = os.getenv("TOGETHER_API", "7e6c200b7b36924bc1b4a5973859a20d2efa7180e9b5c977301173a6c099136b") os.environ["GOOGLE_SEARCH_API_KEY"] = os.getenv("GOOGLE_SEARCH_API_KEY", "AIzaSyBnQwS5kPZGKuWj6sH1aBx5F5bZq0Q5jJk") # Initialize the Vectara index index = VectaraIndex() endpoint = 'https://api.together.xyz/inference' # Load the hallucination evaluation model model_name = "vectara/hallucination_evaluation_model" model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) def search_pubmed(query: str) -> Optional[List[str]]: """ Searches PubMed for a given query and returns a list of formatted results (or None if no results are found). """ Entrez.email = "jayashbhardwaj3@gmail.com" # Replace with your email try: ssl._create_default_https_context = ssl._create_unverified_context handle = Entrez.esearch(db="pubmed", term=query, retmax=3) record = Entrez.read(handle) id_list = record["IdList"] if not id_list: return None handle = Entrez.efetch(db="pubmed", id=id_list, retmode="xml") articles = Entrez.read(handle) results = [] for article in articles['PubmedArticle']: try: medline_citation = article['MedlineCitation'] article_data = medline_citation['Article'] title = article_data['ArticleTitle'] abstract = article_data.get('Abstract', {}).get('AbstractText', [""])[0] result = f"**Title:** {title}\n**Abstract:** {abstract}\n" result += f"**Link:** https://pubmed.ncbi.nlm.gov/{medline_citation['PMID']}\n\n" results.append(result) except KeyError as e: print(f"Error parsing article: {article}, Error: {e}") return results except Exception as e: print(f"Error accessing PubMed: {e}") return None def chat_with_pubmed(article_text, article_link): """ Engages in a chat-like interaction with a PubMed article using TogetherLLM. """ try: llm = TogetherLLM(model="QWEN/QWEN1.5-14B-CHAT", api_key=os.environ['TOGETHER_API']) messages = [ ChatMessage(role=MessageRole.SYSTEM, content="You are a helpful AI assistant summarizing and answering questions about the following medical research article: " + article_link), ChatMessage(role=MessageRole.USER, content=article_text) ] response = llm.chat(messages) return str(response) if response else "I'm sorry, I couldn't generate a summary for this article." except Exception as e: print(f"Error in chat_with_pubmed: {e}") return "An error occurred while generating a summary." def search_web(query: str, num_results: int = 3) -> Optional[List[str]]: """ Searches the web using the Google Search API and returns a list of formatted results (or None if no results are found). """ try: service = build("customsearch", "v1", developerKey=os.environ["GOOGLE_SEARCH_API_KEY"]) # Execute the search request res = service.cse().list(q=query, cx="877170db56f5c4629", num=num_results).execute() if "items" not in res: return None results = [] for item in res["items"]: title = item["title"] link = item["link"] snippet = item["snippet"] result = f"**Title:** {title}\n**Link:** {link}\n**Snippet:** {snippet}\n\n" results.append(result) return results except Exception as e: print(f"Error performing web search: {e}") return None def medmind_chatbot(user_input, chat_history=None): """ Processes user input, interacts with various resources, and generates a response. Handles potential errors, maintains chat history, and evaluates hallucination risk. """ if chat_history is None: chat_history = [] response_parts = [] # Collect responses from different sources try: # Vectara Search try: query_str = user_input response = index.as_query_engine().query(query_str) response_parts.append(f"**MedMind Vectara Knowledge Base Response:**\n{response.response}") except Exception as e: print(f"Error in Vectara search: {e}") response_parts.append("Vectara knowledge base is currently unavailable.") # PubMed Search and Chat pubmed_results = search_pubmed(user_input) if pubmed_results: response_parts.append("**PubMed Articles (Chat & Summarize):**") for article_text in pubmed_results: title, abstract, link = article_text.split("\n")[:3] chat_summary = chat_with_pubmed(abstract, link) response_parts.append(f"{title}\n{chat_summary}\n{link}\n") else: response_parts.append("No relevant PubMed articles found.") # Web Search web_results = search_web(user_input) if web_results: response_parts.append("**Web Search Results:**") response_parts.extend(web_results) else: response_parts.append("No relevant web search results found.") # Combine response parts into a single string response_text = "\n\n".join(response_parts) # Hallucination Evaluation def vectara_hallucination_evaluation_model(text): inputs = tokenizer(text, return_tensors="pt") outputs = model(**inputs) hallucination_probability = outputs.logits[0][0].item() return hallucination_probability hallucination_score = vectara_hallucination_evaluation_model(response_text) HIGH_HALLUCINATION_THRESHOLD = 0.9 if hallucination_score > HIGH_HALLUCINATION_THRESHOLD: response_text = "I'm still under development and learning. I cannot confidently answer this question yet." except Exception as e: print(f"Error in chatbot: {e}") response_text = "An error occurred. Please try again later." chat_history.append((user_input, response_text)) return response_text, chat_history def show_info_popup(): with st.expander("How to use MedMind"): st.write(""" **MedMind is an AI-powered chatbot designed to assist with medical information.** **Capabilities:** * **Answers general medical questions:** MedMind utilizes a curated medical knowledge base to provide answers to a wide range of health-related inquiries. * **Summarizes relevant research articles from PubMed:** The chatbot can retrieve and summarize research articles from the PubMed database, making complex scientific information more accessible. * **Provides insights from a curated medical knowledge base:** Beyond simple answers, MedMind offers additional insights and context from its knowledge base to enhance understanding. * **Perform safe web searches related to your query:** The chatbot can perform web searches using the Google Search API, ensuring the safety and relevance of the results. **Limitations:** * **Not a substitute for professional medical advice:** MedMind is not intended to replace professional medical diagnosis and treatment. Always consult a qualified healthcare provider for personalized medical advice. * **General knowledge and educational purposes:** The information provided by MedMind is for general knowledge and educational purposes only and may not be exhaustive or specific to individual situations. * **Under development:** MedMind is still under development and may occasionally provide inaccurate or incomplete information. It's important to critically evaluate responses and cross-reference with reliable sources. * **Hallucination potential:** While MedMind employs a hallucination evaluation model to minimize the risk of generating fabricated information, there remains a possibility of encountering inaccurate responses, especially for complex or niche queries. **How to use:** 1. **Type your medical question in the text box.** 2. **MedMind will provide a comprehensive response combining information from various sources.** This may include insights from its knowledge base, summaries of relevant research articles, and safe web search results. 3. **You can continue the conversation by asking follow-up questions or providing additional context.** This helps MedMind refine its search and offer more tailored information. 4. **in case the Medmind doesn't show the output please check your internet connection or rerun the same command** 5. **user can either chat with the documents or with generate resposne from vectara + pubmed + web search** 5. **chat with document feature is still under development so it would be better to avoid using it for now** """) # Initialize session state if 'chat_history' not in st.session_state: st.session_state.chat_history = [] # Define function to display chat history with highlighted user input and chatbot response def display_chat_history(): for user_msg, bot_msg in st.session_state.chat_history: st.info(f"**You:** {user_msg}") st.success(f"**MedMind:** {bot_msg}") # Define function to clear chat history def clear_chat(): st.session_state.chat_history = [] def main(): # Streamlit Page Configuration st.set_page_config(page_title="MedMind Chatbot", layout="wide") # Custom Styles st.markdown( """ """, unsafe_allow_html=True, ) # Title and Introduction st.title("MedMind Chatbot") st.write("Ask your medical questions and get reliable information!") # Example Questions (Sidebar) example_questions = [ "What are the symptoms of COVID-19?", "How can I manage my diabetes?", "What are the potential side effects of ibuprofen?", "What lifestyle changes can help prevent heart disease?" ] st.sidebar.header("Example Questions") for question in example_questions: st.sidebar.write(question) # Output Container output_container = st.container() # User Input and Chat History input_container = st.container() with input_container: user_input = st.text_input("You: ", key="input_placeholder", placeholder="Type your medical question here...") new_chat_button = st.button("Start New Chat") if new_chat_button: st.session_state.chat_history = [] # Clear chat history if user_input: response, st.session_state.chat_history = medmind_chatbot(user_input, st.session_state.chat_history) with output_container: display_chat_history() # Information Popup show_info_popup() if __name__ == "__main__": main()