Update app.py

app.py

@@ -1,18 +1,24 @@
+from dotenv import load_dotenv
 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
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 import datetime
-from gradio_client import Client
-import requests
+import uuid
+import random
+import flask
+from flask import request, redirect, make_response
+
+def select_random_name():
+    names = ['Clara', 'Lily']
+    return random.choice(names)
 
+# Example usage
 # Load environment variables
 load_dotenv()
 
-# Configure the Llama index settings with updated API
+# 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",
@@ -27,33 +33,29 @@ Settings.embed_model = HuggingFaceEmbedding(
 
 # 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)
 
-# Function to save chat history to cookies
-def save_chat_history_to_cookies(chat_id, query, response, cookies):
-    if cookies is None:
-        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=None):
+# 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)
+    index.storage_context.persist(persist_dir=PERSIST_DIR)
+
+def handle_query(query):
     chat_text_qa_msgs = [
         (
             "user",
             """
-           You are the Lily 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}
+            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}
             """
@@ -67,12 +69,9 @@ def handle_query(query, cookies=None):
 
     # Use chat history to enhance response
     context_str = ""
-    if cookies:
-        history = cookies.get('chat_history', '[]')
-        history_list = eval(history)
-        for entry in reversed(history_list):
-            if entry["query"].strip():
-                context_str += f"User asked: '{entry['query']}'\nBot answered: '{entry['response']}'\n"
+    for past_query, response in reversed(current_chat_history):
+        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)
@@ -84,68 +83,45 @@ def handle_query(query, cookies=None):
     else:
         response = "Sorry, I couldn't find an answer."
 
-    # Update current chat history dictionary (use unique ID as key)
-    chat_id = str(datetime.datetime.now().timestamp())
-    save_chat_history_to_cookies(chat_id, query, response, cookies)
+    # Update current chat history
+    current_chat_history.append((query, response))
 
     return response
 
-# Define the button click function
-def retrieve_history_and_redirect(cookies):
-    # Initialize the Gradio client
-    client = Client("vilarin/Llama-3.1-8B-Instruct")
-
-    # Retrieve and format chat history
-    history = cookies.get('chat_history', '[]')
-    history_list = eval(history)
-    history_str = "\n".join(
-        [f"User: {entry['query']}\nBot: {entry['response']}" for entry in history_list]
-    )
-
-    # Prepare the message for summarization
-    message = f"""
-    Chat history:
-    {history_str}
-    """
-
-    # Call the Gradio API for summarization
-    result = client.predict(
-        message=message,
-        system_prompt="Summarize the text and provide client interest in 30-40 words in bullet points.",
-        temperature=0.8,
-        max_new_tokens=1024,
-        top_p=1,
-        top_k=20,
-        penalty=1.2,
-        api_name="/chat"
-    )
-
-    # Print the result for debugging
-    print(f"Summary: {result}")
-    
-    # Prepare the data to be sent via POST request
-    data = {
-        "summary": result,
-        "timestamp": str(datetime.datetime.now())
-    }
-
-    # Send the result to the URL
-    response = requests.post("https://redfernstech.com/api/receive_result", json=data)
-
-    # Debugging response status
-    print(f"POST request response: {response.status_code}, {response.text}")
-
-    return response.status_code, response.text
-
-# Define your Gradio chat interface function
+# Example usage: Process PDF ingestion from directory
+print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
+data_ingestion_from_directory()
+
+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 chat_interface(message, history):
-    cookies = {}  # You might need to get cookies from the request in a real implementation
     try:
-        # Process the user message and generate a response
-        response = handle_query(message, cookies)
-
-        # Return the bot response
-        return response
+        # 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",
+            "message": message,
+            "response": response,
+            "timestamp": datetime.datetime.now().isoformat()  # Use a library like datetime
+        }
+
+        # Store chat history in cookies (for demo purposes)
+        resp = make_response(response)
+        resp.set_cookie('chat_history', str(current_chat_history))
+        return resp
     except Exception as e:
         return str(e)
 
@@ -174,26 +150,25 @@ 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.svelte-1rjryqp{display: none;}
 div.progress-text.svelte-z7cif2.meta-text {display: none;}
 '''
-
-# Use Gradio Blocks to wrap components
-with gr.Blocks(css=css) as demo:
-    chatbot = gr.Chatbot(placeholder="<strong>Your Personal Yes-Man</strong><br>Ask Me Anything")
-    with gr.Row():  # Align horizontally in the same line
-        with gr.Column(scale=8):  # 80% for the input and submit button
-            submit_button = gr.ChatInterface(fn=chat_interface, chatbot=chatbot, clear_btn=None, undo_btn=None, retry_btn=None)
-        with gr.Column(scale=2):  # 20% for custom button
-            custom_button = gr.Button("Close Chat")
-
-    # Connect the button with the function, and handle the redirection
-    custom_button.click(fn=retrieve_history_and_redirect, inputs=[gr.State()])
-    
-    # Add a JavaScript function to handle redirection after the Gradio event is processed
-    custom_button.click(fn=None, js="() => { window.open('https://redfernstech.com/chat-bot-test', '_blank'); }")
-
-# Launch the Gradio interface
-demo.launch()
+def redirect_page():
+    return redirect("https://example.com")  # Replace with your target URL
+
+gr.Interface(
+    fn=chat_interface,
+    inputs="text",
+    outputs="html",
+    live=True,
+    css=css,
+    description="<button onclick='window.location.href=\"https://example.com\"'>Redirect to another page</button>"
+).launch()