Update app.py

app.py

@@ -1,27 +1,29 @@
 from dotenv import load_dotenv
-from fastapi import FastAPI, Request
 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
+from sentence_transformers import SentenceTransformer
 import firebase_admin
 from firebase_admin import db, credentials
 import datetime
 import uuid
 import random
-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 threading
-import uvicorn
 
+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/"})
-
-app = FastAPI()
-
+# Configure the Llama index settings
 Settings.llm = HuggingFaceInferenceAPI(
     model_name="meta-llama/Meta-Llama-3-8B-Instruct",
     tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
@@ -34,17 +36,19 @@ Settings.embed_model = HuggingFaceEmbedding(
     model_name="BAAI/bge-small-en-v1.5"
 )
 
-
-# Define directories
+# Define the directory for persistent storage and data
 PERSIST_DIR = "db"
-PDF_DIRECTORY = 'data'
+PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
+
+# Ensure directories exist
 os.makedirs(PDF_DIRECTORY, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
-# Variable to store chat history
+# 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)
@@ -55,7 +59,7 @@ def handle_query(query):
         (
             "user",
             """
-            You are Clara, the Redfernstech chatbot. Provide accurate, professional, and concise answers based on the data. Respond 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}
@@ -64,9 +68,11 @@ def handle_query(query):
     ]
     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():
@@ -75,72 +81,99 @@ def handle_query(query):
     query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)
     answer = query_engine.query(query)
 
-    response = answer.response if hasattr(answer, 'response') else "Sorry, I couldn't find an answer."
+    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(username, email, session_id, message_data):
-    ref = db.reference(f'/chat_history/{username}/{email}/{session_id}')
+# Example usage: Process PDF ingestion from directory
+print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
+data_ingestion_from_directory()
+# 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)
 
-def chat_interface(message, history, request: gr.Request):
+# Define your Gradio chat interface function (replace with your actual logic)
+def chat_interface(message, history):
     try:
-        username = request.query_params.get('username')
-        email = request.query_params.get('email')
+        # 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": request.client.host,
+            "sender": "user",
             "message": message,
             "response": response,
-            "timestamp": datetime.datetime.now().isoformat()
+            "timestamp": datetime.datetime.now().isoformat()  # Use a library like datetime
         }
 
-        save_chat_message(username, email, session_id, message_data)
+        # 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: 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;}
+.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.get("/chat")
 async def chat_ui(username: str, email: str):
     gr.ChatInterface(
@@ -154,5 +187,4 @@ async def chat_ui(username: str, email: str):
     return {"message": "Chat interface launched."}
 
 if __name__ == "__main__":
-    data_ingestion_from_directory()
-    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()