Create app.py

app.py

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+import os
+import faiss
+import pickle
+import streamlit as st
+from groq import Groq
+from sentence_transformers import SentenceTransformer
+
+# Initialize the Groq client for Llama LLM
+GROQ_API_KEY = "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"
+client = Groq(
+    api_key=GROQ_API_KEY,
+)
+
+# Load FAISS index and metadata
+def load_faiss_index(index_path, metadata_path):
+    if not os.path.exists(index_path) or not os.path.exists(metadata_path):
+        raise FileNotFoundError("FAISS index or metadata file not found!")
+    index = faiss.read_index(index_path)
+    with open(metadata_path, "rb") as f:
+        metadata = pickle.load(f)
+    return index, metadata
+
+# Load the FAISS index and metadata
+index, metadata = load_faiss_index("faiss_index.bin", "metadata.pkl")
+
+# Initialize the SentenceTransformer for embedding
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Streamlit UI Configuration
+st.set_page_config(page_title="RAG Chatbot: Healthcare, Education & Finance", layout="wide")
+st.title("🤖 RAG Chatbot: Healthcare, Education & Finance")
+st.markdown("Welcome to the **AI Chatbot**! Ask anything about **healthcare**, **education**, or **finance**.")
+
+# Initialize session state for chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Input field for user queries
+user_input = st.text_input("💬 Type your message:", placeholder="Ask me anything...")
+
+# Function to generate Llama LLM responses using Groq API
+def generate_llama_response(user_query, context):
+    response = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": "You are an expert assistant in Healthcare, Education, and Finance."},
+            {"role": "user", "content": f"Context: {context}\n\nQuery: {user_query}"}
+        ],
+        model="llama-3.3-70b-versatile",
+        stream=False,
+    )
+    return response.choices[0].message.content
+
+# Function to handle user queries and retrieve responses
+def handle_query(user_query):
+    # Embed the user query and search FAISS
+    query_vector = embedder.encode([user_query])
+    _, top_indices = index.search(query_vector, k=3)
+    relevant_contexts = [metadata[i] for i in top_indices[0]]
+    
+    # Combine retrieved chunks into context
+    context = "\n".join(relevant_contexts)
+    
+    # Generate response from Llama LLM
+    response = generate_llama_response(user_query, context)
+    
+    # Save user input and response to FAISS for tracking
+    user_vector = embedder.encode([user_query])
+    index.add(user_vector)
+    metadata.append(user_query)
+    faiss.write_index(index, "faiss_index.bin")
+    with open("metadata.pkl", "wb") as f:
+        pickle.dump(metadata, f)
+    
+    return response
+
+# Process user input
+if user_input:
+    # Display user message
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    
+    # Get bot response
+    bot_response = handle_query(user_input)
+    st.session_state.messages.append({"role": "assistant", "content": bot_response})
+
+# Display chat messages
+for message in st.session_state.messages:
+    if message["role"] == "user":
+        st.markdown(f"**You:** {message['content']}")
+    else:
+        st.markdown(f"**Bot:** {message['content']}")