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Delete app.py

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  1. app.py +0 -179
app.py DELETED
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- import os
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- import streamlit as st
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- import numpy as np
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- import faiss
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- from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModel
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- from groq import Groq
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-
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- # Load API Key from Environment
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- groq_api_key = os.environ.get("GROQ_API_KEY")
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- if groq_api_key is None:
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- st.error("GROQ_API_KEY environment variable not set.")
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- st.stop()
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-
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- # Initialize Groq Client
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- try:
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- client = Groq(api_key=groq_api_key)
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- except Exception as e:
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- st.error(f"Error initializing Groq client: {e}")
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- st.stop()
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-
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- # Load PubMedBERT Model (Try Groq API first, then Hugging Face)
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- try:
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- pubmedbert_tokenizer = AutoTokenizer.from_pretrained("NeuML/pubmedbert-base-embeddings")
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- pubmedbert_model = AutoModel.from_pretrained("NeuML/pubmedbert-base-embeddings")
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- pubmedbert_pipeline = pipeline('feature-extraction', model=pubmedbert_model, tokenizer=pubmedbert_tokenizer, device=-1)
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- except Exception:
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- st.warning("Error loading PubMedBERT from Groq API. Using Hugging Face model.")
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- pubmedbert_tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext")
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- pubmedbert_model = AutoModelForSequenceClassification.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext")
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- pubmedbert_pipeline = pipeline('feature-extraction', model=pubmedbert_model, tokenizer=pubmedbert_tokenizer, device=-1)
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-
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- # Initialize FAISS Index
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- embedding_dim = 768
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- index = faiss.IndexFlatL2(embedding_dim)
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-
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- # Function to Check if Query is Related to Epilepsy
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- def preprocess_query(query):
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- tokens = query.lower().split()
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- epilepsy_keywords = ["seizure", "epilepsy", "convulsion", "neurology", "brain activity"]
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-
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- is_epilepsy_related = any(k in tokens for k in epilepsy_keywords)
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-
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- return tokens, is_epilepsy_related
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-
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- # Function to Generate Response with Chat History
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- def generate_response(user_query, chat_history):
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- # Grammatical Correction using LLaMA (Hidden from User)
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- try:
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- correction_prompt = f"""
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- Correct the following user query for grammar and spelling errors, but keep the original intent intact.
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- Do not add or remove any information, just fix the grammar.
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- User Query: {user_query}
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- Corrected Query:
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- """
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- grammar_completion = client.chat.completions.create(
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- messages=[{"role": "user", "content": correction_prompt}],
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- model="llama-3.3-70b-versatile",
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- stream=False,
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- )
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- corrected_query = grammar_completion.choices[0].message.content.strip()
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- # If correction fails or returns empty, use original query
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- if not corrected_query:
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- corrected_query = user_query
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- except Exception as e:
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- corrected_query = user_query # Fallback to original query if correction fails
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- print(f"⚠️ Grammar correction error: {e}") # Optional: Log the error for debugging
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-
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- tokens, is_epilepsy_related = preprocess_query(corrected_query) # Use corrected query for processing
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-
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- # Greeting Responses
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- greetings = ["hello", "hi", "hey"]
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- if any(word in tokens for word in greetings):
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- return "👋 Hello! How can I assist you today?"
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-
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- # If Epilepsy Related - Use Epilepsy Focused Response
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- if is_epilepsy_related:
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- # Try Getting Medical Insights from PubMedBERT
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- try:
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- pubmedbert_embeddings = pubmedbert_pipeline(corrected_query) # Use corrected query for PubMedBERT
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- embedding_mean = np.mean(pubmedbert_embeddings[0], axis=0)
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- index.add(np.array([embedding_mean]))
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- pubmedbert_insights = "**PubMedBERT Analysis:** ✅ Query is relevant to epilepsy research."
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- except Exception as e:
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- pubmedbert_insights = f"⚠️ Error during PubMedBERT analysis: {e}"
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-
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- # Use LLaMA for Final Response Generation with Chat History Context (Epilepsy Focus)
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- try:
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- prompt_history = ""
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- if chat_history:
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- prompt_history += "**Chat History:**\n"
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- for message in chat_history:
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- prompt_history += f"{message['role'].capitalize()}: {message['content']}\n"
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- prompt_history += "\n"
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-
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- epilepsy_prompt = f"""
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- {prompt_history}
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- **User Query:** {corrected_query} # Use corrected query for final response generation
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- **Instructions:** Provide a concise, structured, and human-friendly response specifically about epilepsy or seizures, considering the conversation history if available.
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- """
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-
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- chat_completion = client.chat.completions.create(
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- messages=[{"role": "user", "content": epilepsy_prompt}],
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- model="llama-3.3-70b-versatile",
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- stream=False,
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- )
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- model_response = chat_completion.choices[0].message.content.strip()
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- except Exception as e:
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- model_response = f"⚠️ Error generating response with LLaMA: {e}"
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-
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- return f"**NeuroGuard:** ✅ **Analysis:**\n{pubmedbert_insights}\n\n**Response:**\n{model_response}"
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-
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-
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- # If Not Epilepsy Related - Try to Answer as General Health Query
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- else:
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- # Try Getting Medical Insights from PubMedBERT (even for general health)
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- try:
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- pubmedbert_embeddings = pubmedbert_pipeline(corrected_query)
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- embedding_mean = np.mean(pubmedbert_embeddings[0], axis=0)
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- index.add(np.array([embedding_mean]))
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- pubmedbert_insights = "**PubMedBERT Analysis:** PubMed analysis performed for health-related context." # General analysis message
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- except Exception as e:
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- pubmedbert_insights = f"⚠️ Error during PubMedBERT analysis: {e}"
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-
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- # Use LLaMA for General Health Response Generation with Chat History Context
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- try:
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- prompt_history = ""
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- if chat_history:
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- prompt_history += "**Chat History:**\n"
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- for message in chat_history:
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- prompt_history += f"{message['role'].capitalize()}: {message['content']}\n"
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- prompt_history += "\n"
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-
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- general_health_prompt = f"""
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- {prompt_history}
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- **User Query:** {corrected_query}
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- **Instructions:** Provide a concise, structured, and human-friendly response to the general health query, considering the conversation history if available. If the query is clearly not health-related, respond generally.
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- """
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-
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- chat_completion = client.chat.completions.create(
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- messages=[{"role": "user", "content": general_health_prompt}],
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- model="llama-3.3-70b-versatile",
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- stream=False,
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- )
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- model_response = chat_completion.choices[0].message.content.strip()
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- except Exception as e:
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- model_response = f"⚠️ Error generating response with LLaMA: {e}"
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-
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- return f"**NeuroGuard:** ✅ **Analysis:**\n{pubmedbert_insights}\n\n**Response:**\n{model_response}"
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-
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-
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- # Streamlit UI Setup
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- st.set_page_config(page_title="NeuroGuard: Epilepsy & Health Chatbot", layout="wide") # Updated title
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- st.title("🧠 NeuroGuard: Epilepsy & Health Chatbot") # Updated title
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- st.write("💬 Ask me anything about epilepsy, seizures, and general health. I remember our conversation!") # Updated description
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-
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- # Initialize Chat History in Session State
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- if "chat_history" not in st.session_state:
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- st.session_state.chat_history = []
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-
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- # Display Chat History
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- for message in st.session_state.chat_history:
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- with st.chat_message(message["role"]):
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- st.markdown(message["content"])
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-
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- # User Input
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- if prompt := st.chat_input("Type your question here..."):
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- st.session_state.chat_history.append({"role": "user", "content": prompt})
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- with st.chat_message("user"):
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- st.markdown(prompt)
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-
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- # Generate Bot Response
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- with st.chat_message("bot"):
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- with st.spinner("🤖 Thinking..."):
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- try:
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- response = generate_response(prompt, st.session_state.chat_history) # Pass chat history here
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- st.markdown(response)
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- st.session_state.chat_history.append({"role": "bot", "content": response})
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- except Exception as e:
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- st.error(f"⚠️ Error processing query: {e}")