Update app.py

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 import os
+from http.cookies import SimpleCookie
 from dotenv import load_dotenv
 from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
@@ -31,18 +32,19 @@ PDF_DIRECTORY = 'data'
 os.makedirs(PDF_DIRECTORY, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
-# Variable to store current chat conversation in a dictionary
-current_chat_history = {}
-kkk = random.choice(['Clara', 'Lily'])
-
-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):
+# Function to save chat history to cookies
+def save_chat_history_to_cookies(chat_id, query, response, cookies):
+    history = cookies.get('chat_history', '[]')
+    history_list = eval(history)
+    history_list.append({
+        "chat_id": chat_id,
+        "query": query,
+        "response": response,
+        "timestamp": str(datetime.datetime.now())
+    })
+    cookies['chat_history'] = str(history_list)
+
+def handle_query(query, cookies):
     chat_text_qa_msgs = [
         (
             "user",
@@ -62,7 +64,7 @@ def handle_query(query):
 
     # Use chat history to enhance response
     context_str = ""
-    for past_query, response in reversed(current_chat_history.values()):
+    for past_query, response in reversed(cookies.get('chat_history', [])):
         if past_query.strip():
             context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"
 
@@ -78,15 +80,15 @@ def handle_query(query):
 
     # Update current chat history dictionary (use unique ID as key)
     chat_id = str(datetime.datetime.now().timestamp())
-    current_chat_history[chat_id] = (query, response)
+    save_chat_history_to_cookies(chat_id, query, response, cookies)
 
     return response
 
 # Define your Gradio chat interface function
-def chat_interface(message, history):
+def chat_interface(message, history, cookies):
     try:
         # Process the user message and generate a response
-        response = handle_query(message)
+        response = handle_query(message, cookies)
 
         # Return the bot response
         return response
@@ -124,19 +126,7 @@ div.svelte-1rjryqp{display: none;}
 div.progress-text.svelte-z7cif2.meta-text {display: none;}
 '''
 
-# JavaScript to handle chat history saving
-js_code = '''
-<script>
-function saveHistory(message, response) {
-    // Store message and response in session storage
-    let history = JSON.parse(sessionStorage.getItem('chatHistory') || '[]');
-    history.push({message, response});
-    sessionStorage.setItem('chatHistory', JSON.stringify(history));
-}
-</script>
-'''
-
 # Use Gradio Blocks to wrap components
-chat = gr.ChatInterface(chat_interface, css=css, clear_btn=None, undo_btn=None, retry_btn=None, js=js_code).launch()
+chat = gr.ChatInterface(chat_interface, css=css, clear_btn=None, undo_btn=None, retry_btn=None).launch()
 
 # Launch the Gradio interface