Update app.py

app.py

@@ -1,35 +1,100 @@
-import gradio as gr
-from fastapi import FastAPI, Request, Depends, HTTPException
+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse
 from fastapi.staticfiles import StaticFiles
-import uvicorn
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
+from llama_index.llms.huggingface import HuggingFaceInferenceAPI
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+import os
+import datetime
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
 
-# Initialize FastAPI app
 app = FastAPI()
 
-# Define a function for the chat interface
-def respond_to_chat(history, message):
-    response = f"Hello, {message}!"
-    return response, history
+# Serve static files (HTML, CSS, JS)
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+# Configure Llama index settings
+Settings.llm = HuggingFaceInferenceAPI(
+    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
+    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
+    context_window=3000,
+    token=os.getenv("HF_TOKEN"),
+    max_new_tokens=512,
+    generate_kwargs={"temperature": 0.1},
+)
+Settings.embed_model = HuggingFaceEmbedding(
+    model_name="BAAI/bge-small-en-v1.5"
+)
+
+PERSIST_DIR = "db"
+PDF_DIRECTORY = 'data'
+
+# Ensure directories exist
+os.makedirs(PDF_DIRECTORY, exist_ok=True)
+os.makedirs(PERSIST_DIR, exist_ok=True)
+
+def data_ingestion_from_directory():
+    documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
+    storage_context = StorageContext.from_defaults()
+    index = VectorStoreIndex.from_documents(documents)
+    index.storage_context.persist(persist_dir=PERSIST_DIR)
+
+data_ingestion_from_directory()  # Process PDF ingestion at startup
 
-# Create Gradio ChatInterface
-chat = gr.ChatInterface(fn=respond_to_chat, title="Chat with AI")
+# Store chat history in-memory (you could also use a database)
+chat_history = []
 
-# Mount static files
-app.mount("/static", StaticFiles(directory="static", html=True), name="static")
+def handle_query(query):
+    chat_text_qa_msgs = [
+        (
+            "user",
+            f"""
+            You are the Clara Redfernstech chatbot. Your goal is to provide accurate, professional, and helpful answers to user queries based on the company's data. Always ensure your responses are clear and concise. Give response within 10-15 words only       
+            {context_str}
+            Question:
+            {query}
+            """
+        )
+    ]
+    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
 
-# Function to authenticate users
-def authenticate(username: str, password: str):
-    if username == "your_username" and password == "your_password":
-        return True
-    return False
+    # Load index from storage
+    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+    index = load_index_from_storage(storage_context)
 
-@app.get("/gradio")
-async def gradio_access(username: str, password: str):
-    if authenticate(username, password):
-        return gr.Interface.load("https://srinukethanaboina-srunu.hf.space/gradio")  # Adjust URL if needed
+    # Use chat history to enhance response
+    context_str = "\n".join([f"User asked: '{msg['message']}'\nBot answered: '{msg['response']}'" for msg in chat_history])
+
+    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)
+    answer = query_engine.query(query)
+
+    if hasattr(answer, 'response'):
+        return answer.response
+    elif isinstance(answer, dict) and 'response' in answer:
+        return answer['response']
     else:
-        raise HTTPException(status_code=401, detail="Unauthorized")
+        return "Sorry, I couldn't find an answer."
+
+@app.get("/", response_class=HTMLResponse)
+async def read_root():
+    with open("static/index.html") as f:
+        return f.read()
+
+@app.post("/chat/")
+async def chat(message: str):
+    response = handle_query(message)
+    
+    message_data = {
+        "sender": "User",
+        "message": message,
+        "response": response,
+        "timestamp": datetime.datetime.now().isoformat()
+    }
+
+    # Store the interaction in chat history
+    chat_history.append(message_data)
 
-# Run the app with uvicorn
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    return {"response": response}