Update app.py

app.py

@@ -4,21 +4,23 @@ import os
 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 random
-import uuid
+from sentence_transformers import SentenceTransformer
+import firebase_admin
+from firebase_admin import db, credentials
 import datetime
-from gradio_client import Client
-
-# Initialize Gradio Client
-client = Client("srinukethanaboina/SRUNU")
+import uuid
+import random
 
 def select_random_name():
     names = ['Clara', 'Lily']
     return random.choice(names)
 
+# Example usage
 # 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 the Llama index settings
 Settings.llm = HuggingFaceInferenceAPI(
     model_name="meta-llama/Meta-Llama-3-8B-Instruct",
@@ -34,7 +36,7 @@ Settings.embed_model = HuggingFaceEmbedding(
 
 # Define the directory for persistent storage and data
 PERSIST_DIR = "db"
-PDF_DIRECTORY = 'data'  # Directory containing PDFs
+PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
 
 # Ensure directories exist
 os.makedirs(PDF_DIRECTORY, exist_ok=True)
@@ -55,7 +57,7 @@ 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 response within 10-15 words only       
             {context_str}
             Question:
             {query_str}
@@ -93,35 +95,43 @@ def handle_query(query):
 print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
 data_ingestion_from_directory()
 
-def predict(message, req):
+# 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>
     '''
-    # Use the gradio_client API to process the chat history and IP address
-    response = client.predict(
-        ip_address=req.client.host,  # Extract IP address from request
-        chat_history=message,
-        api_name="/predict"
-    )
+    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, request: gr.Request):
+def chat_interface(message, history,request: gr.Request):
     try:
-        # Process the user message and generate a response
-        response = predict(message, request)
+        # 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",
+            "sender": request.client.host,
             "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:
@@ -163,12 +173,8 @@ label.svelte-1b6s6s {display: none}
 div.svelte-rk35yg {display: none;}
 div.progress-text.svelte-z7cif2.meta-text {display: none;}
 '''
-
-# Launch the Gradio interface
-# Launch the Gradio interface with verbose error reporting
 gr.ChatInterface(chat_interface,
                  css=css,
                  description="Clara",
                  clear_btn=None, undo_btn=None, retry_btn=None,
-                 ).launch(show_error=True)
-
+                 ).launch()