Update app.py

app.py

@@ -4,24 +4,20 @@ 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
-from sentence_transformers import SentenceTransformer
 import firebase_admin
 from firebase_admin import db, credentials
 import datetime
 import uuid
 import random
-session_id = str(uuid.uuid4())
-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
+
+# Initialize Firebase
+cred = credentials.Certificate("path_to_your_firebase_adminsdk_json.json")
+firebase_admin.initialize_app(cred, {"databaseURL": "https://your-project-id.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",
@@ -34,30 +30,33 @@ Settings.embed_model = HuggingFaceEmbedding(
     model_name="BAAI/bge-small-en-v1.5"
 )
 
-# Define the directory for persistent storage and data
+# Define directories
 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)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
-# Variable to store current chat conversation
-current_chat_history = []
+# Variable to store chat history
+session_chat_histories = {}
+
+def select_random_name():
+    names = ['Clara', 'Lily']
+    return random.choice(names)
 
 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):
+def handle_query(session_id, query):
     chat_text_qa_msgs = [
         (
             "user",
             """
-            As Clera, your goal is to provide code to the user.
+            As Clara, your goal is to provide code to the user.
             Your task is to give code to the model and offer guidance on creating a website using Django, HTML, CSS, and Bootstrap.
             {context_str}
             Question:
@@ -67,15 +66,14 @@ 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():
-            context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"
+    if session_id in session_chat_histories:
+        for past_query, response in reversed(session_chat_histories[session_id]):
+            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)
@@ -87,83 +85,60 @@ def handle_query(query):
     else:
         response = "Sorry, I couldn't find an answer."
 
-    # Update current chat history
-    current_chat_history.append((query, response))
+    if session_id not in session_chat_histories:
+        session_chat_histories[session_id] = []
+
+    session_chat_histories[session_id].append((query, response))
     message_data = {
-            "query": query,
-            "response":response,
-            "timestamp": datetime.datetime.now().isoformat()  # Use a library like datetime
-        }
+        "query": query,
+        "response": response,
+        "timestamp": datetime.datetime.now().isoformat()
+    }
 
-        # Call the save function to store in Firebase with the generated session ID
     save_chat_message(session_id, message_data)
-
     return response
 
-# 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 = db.reference(f'/chat_history/{session_id}')
     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
-        
+    session_id = history[0][1] if history else str(uuid.uuid4())
+    response = handle_query(session_id, message)
+    return response, [(message, response)]
 
-        # Process the user message and generate a response (your chatbot logic)
-        response = handle_query(message)
-
-        # 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;
+    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;
   }
-label.svelte-1b6s6s {display: none}
+  .response-with-logo {
+    display: flex;
+    align-items: center;
+    margin-bottom: 10px;
+  }
+  footer {
+      display: none !important;
+      background-color: #F8D7DA;
+    }
+  label.svelte-1b6s6s {display: none}
 '''
-gr.ChatInterface(chat_interface,
-                 css=css,
-                 description="Clara",
-                 clear_btn=None, undo_btn=None, retry_btn=None,
-                 ).launch()
+
+# Load data and start Gradio interface
+print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
+data_ingestion_from_directory()
+
+gr.ChatInterface(fn=chat_interface, 
+                 css=css, 
+                 description="Clara", 
+                 clear_btn=None, undo_btn=None, retry_btn=None).launch()