Upload app.py

app.py

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