Update app.py

app.py

@@ -1,183 +1,8 @@
-from dotenv import load_dotenv
+from fastapi import FastAPI
 import gradio as gr
-import os
-import uvicorn
-from fastapi import FastAPI, Request
-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 firebase_admin
-from firebase_admin import db, credentials
-import datetime
-import uuid
-import threading
-import random
-
-# Function to select a random name
-def select_random_name():
-    names = ['Clara', 'Lily']
-    return random.choice(names)
-
-# Load environment variables
-load_dotenv()
-
-# Authenticate to Firebase
-cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json")
-firebase_admin.initialize_app(cred, {"databaseURL": "https://redfernstech-fd8fe-default-rtdb.firebaseio.com/"})
-
-# 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"
-)
-
-# Define the directory for persistent storage and data
-PERSIST_DIR = "db"
-PDF_DIRECTORY = 'data'
-
-# Ensure directories exist
-os.makedirs(PDF_DIRECTORY, exist_ok=True)
-os.makedirs(PERSIST_DIR, exist_ok=True)
-
-# Variable to store current chat conversation
-current_chat_history = []
-
-def data_ingestion_from_directory():
-    # Use SimpleDirectoryReader on the directory containing the PDF files
-    documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
-    storage_context = StorageContext.from_defaults()
-    index = VectorStoreIndex.from_documents(documents)
-    index.storage_context.persist(persist_dir=PERSIST_DIR)
-
-def handle_query(query):
-    chat_text_qa_msgs = [
-        (
-            "user",
-            """
-            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 responses within 10-15 words only.
-            {context_str}
-            Question:
-            {query_str}
-            """
-        )
-    ]
-    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
-
-    # Load index from storage
-    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-    index = load_index_from_storage(storage_context)
-
-    # Use chat history to enhance response
-    context_str = ""
-    for past_query, response in reversed(current_chat_history):
-        if past_query.strip():
-            context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"
-
-    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'):
-        response = answer.response
-    elif isinstance(answer, dict) and 'response' in answer:
-        response = answer['response']
-    else:
-        response = "Sorry, I couldn't find an answer."
-
-    # Update current chat history
-    current_chat_history.append((query, response))
-
-    return response
-
-def save_chat_message(session_id, message_data):
-    ref = db.reference(f'/chat_history/{session_id}')  # Use the session ID to save chat data
-    ref.push().set(message_data)
-
-def chat_interface(message, history):
-    try:
-        # Generate a unique session ID for this chat session
-        session_id = str(uuid.uuid4())
-
-        # Process the user message and generate a response (your chatbot logic)
-        response = handle_query(message)
-
-        # Capture the message data
-        message_data = {
-            "sender": "user",
-            "message": message,
-            "response": response,
-            "timestamp": datetime.datetime.now().isoformat()  # Use a library like datetime
-        }
-
-        # Call the save function to store in Firebase with the generated session ID
-        save_chat_message(session_id, message_data)
-
-        # Return the bot response
-        return response
-    except Exception as e:
-        return str(e)
-
-# Custom CSS for styling
-css = '''
-  .circle-logo {
-  display: inline-block;
-  width: 40px;
-  height: 40px;
-  border-radius: 50%;
-  overflow: hidden;
-  margin-right: 10px;
-  vertical-align: middle;
-}
-.circle-logo img {
-  width: 100%;
-  height: 100%;
-  object-fit: cover;
-}
-.response-with-logo {
-  display: flex;
-  align-items: center;
-  margin-bottom: 10px;
-}
-footer {
-    display: none !important;
-    background-color: #F8D7DA;
-  }
-.svelte-1ed2p3z p {
-  font-size: 24px;
-  font-weight: bold;
-  line-height: 1.2;
-  color: #111;
-  margin: 20px 0;
-}
-label.svelte-1b6s6s {display: none}
-div.svelte-rk35yg {display: none;}
-div.progress-text.svelte-z7cif2.meta-text {display: none;}
-'''
-
 app = FastAPI()
-
 @app.get("/")
-async def root():
-    return {"message": "Hello"}
-
-@app.get("/chat")
-async def chat_ui(username: str, email: str):
-    gr.ChatInterface(
-        fn=chat_interface,
-        css=css,
-        description="Clara",
-        clear_btn=None,
-        undo_btn=None,
-        retry_btn=None
-    ).launch()
-    return {"message": "Chat interface launched."}
-
-if __name__ == "__main__":
-    threading.Thread(target=lambda: uvicorn.run(app, host="0.0.0.0", port=8000), daemon=True).start()
+def read_main():
+    return {"message": "This is your main app"}
+io = gr.ChatInterface(respond_to_chat)
+app = gr.mount_gradio_app(app, io, path="/gradio")