Create app.py

app.py

@@ -0,0 +1,75 @@
+import os
+import streamlit as st
+import faiss
+import pickle
+from groq import Groq
+from datasets import load_dataset
+from transformers import pipeline
+
+# Initialize Groq API
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Load datasets
+healthcare_ds = load_dataset("harishnair04/mtsamples")
+education_ds = load_dataset("ehovy/race", "all")
+finance_ds = load_dataset("warwickai/financial_phrasebank_mirror")
+
+# Load chat model
+chat_pipe = pipeline("text-generation", model="rajkumarrrk/dialogpt-fine-tuned-on-daily-dialog")
+
+# FAISS Index Setup
+index = faiss.IndexFlatL2(768)
+chat_history = []
+
+# Streamlit UI Setup
+st.set_page_config(page_title="AI Chatbot", layout="wide")
+st.title("🤖 AI Chatbot (Healthcare, Education & Finance)")
+
+# Sidebar for chat history
+st.sidebar.title("📜 Chat History")
+if st.sidebar.button("Download Chat History"):
+    with open("chat_history.txt", "w") as file:
+        file.write("\n".join(chat_history))
+    st.sidebar.success("Chat history saved!")
+
+# Chat Interface
+user_input = st.text_input("💬 Ask me anything:", placeholder="Type your query here...")
+if st.button("Send"):
+    if user_input:
+        # Determine dataset based on user query (Basic CAG Implementation)
+        dataset = healthcare_ds if "health" in user_input.lower() else \
+                  education_ds if "education" in user_input.lower() else \
+                  finance_ds
+        
+        # RAG: Retrieve relevant data
+        retrieved_data = dataset['train'][0]  # Simplified retrieval
+        
+        # Generate response using Llama via Groq API
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": f"{user_input} {retrieved_data}"}],
+            model="llama-3.3-70b-versatile"
+        )
+        response = chat_completion.choices[0].message.content
+        
+        # Save chat to FAISS and display
+        chat_history.append(f"User: {user_input}\nBot: {response}")
+        st.text_area("🤖 AI Response:", value=response, height=200)
+
+# Display past chats
+st.sidebar.write("\n".join(chat_history))
+
+# Save chat history using pickle for persistence
+def save_chat_history():
+    with open("chat_history.pkl", "wb") as file:
+        pickle.dump(chat_history, file)
+
+def load_chat_history():
+    global chat_history
+    if os.path.exists("chat_history.pkl"):
+        with open("chat_history.pkl", "rb") as file:
+            chat_history = pickle.load(file)
+
+load_chat_history()
+if st.sidebar.button("Save Chat History"):
+    save_chat_history()
+    st.sidebar.success("Chat history saved permanently!")