Update app.py

app.py

@@ -6,7 +6,6 @@ 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
-from sentence_transformers import SentenceTransformer
 import firebase_admin
 from firebase_admin import db, credentials
 import datetime
@@ -14,17 +13,19 @@ import uuid
 import threading
 import random
 
+# Function to select a random name
 def select_random_name():
     names = ['Clara', 'Lily']
     return random.choice(names)
 
-# Example usage
 # Load environment variables
 load_dotenv()
-# authenticate to firebase
+
+# 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 the Llama index settings
+
+# 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",
@@ -33,13 +34,14 @@ Settings.llm = HuggingFaceInferenceAPI(
     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'  # Changed to the directory containing PDFs
+PDF_DIRECTORY = 'data'
 
 # Ensure directories exist
 os.makedirs(PDF_DIRECTORY, exist_ok=True)
@@ -60,7 +62,7 @@ def handle_query(query):
         (
             "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 response within 10-15 words only       
+            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}
@@ -94,28 +96,10 @@ def handle_query(query):
 
     return response
 
-# Example usage: Process PDF ingestion from directory
-print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
-data_ingestion_from_directory()
-app = FastAPI()
-# Define the function to handle predictions
-"""def predict(message,history):
-    response = handle_query(message)
-    return response"""
-def predict(message, history):
-    logo_html = '''
-    <div class="circle-logo">
-      <img src="https://rb.gy/8r06eg" alt="FernAi">
-    </div>
-    '''
-    response = handle_query(message)
-    response_with_logo = f'<div class="response-with-logo">{logo_html}<div class="response-text">{response}</div></div>'
-    return response_with_logo
 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)
 
-# Define your Gradio chat interface function (replace with your actual logic)
 def chat_interface(message, history):
     try:
         # Generate a unique session ID for this chat session
@@ -176,17 +160,24 @@ 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()
+        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()
+    threading.Thread(target=lambda: uvicorn.run(app, host="0.0.0.0", port=8000), daemon=True).start()