Update app.py

app.py

@@ -4,7 +4,6 @@ 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
-from simple_salesforce import Salesforce, SalesforceLogin
 import random
 import datetime
 
@@ -48,7 +47,7 @@ def handle_query(query):
         (
             "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       
+           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}
             Question:
             {query_str}
@@ -83,42 +82,6 @@ def handle_query(query):
 
     return response
 
-def save_chat_history_to_salesforce():
-    username =os.getenv("username")
-    password =os.getenv("password")
-    security_token =os.getenv("security_token")
-    domain = 'test' 
-
-    # Log in to Salesforce
-    session_id, sf_instance = SalesforceLogin(username=username, password=password, security_token=security_token, domain=domain)
-
-    # Create Salesforce object
-    sf = Salesforce(instance=sf_instance, session_id=session_id)
-
-    # Iterate over chat history dictionary and push to Salesforce
-    for chat_id, (user_message, bot_response) in current_chat_history.items():
-        data = {
-            'Name': 'Chat with user',
-            'Bot_Message__c': bot_response,
-            'User_Message__c': user_message,
-            'Date__c': str(datetime.datetime.now().date())
-        }
-        # Insert into the custom object (replace 'Chat_History__c' with your custom object's API name)
-        sf.Chat_History__c.create(data)
-
-# Define the function to handle predictions
-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 the response with the logo
-    return response_with_logo
-
 # Define your Gradio chat interface function
 def chat_interface(message, history):
     try:
@@ -163,11 +126,8 @@ div.progress-text.svelte-z7cif2.meta-text {display: none;}
 
 # Use Gradio Blocks to wrap components
 with gr.Blocks() as demo:
-    chat = gr.ChatInterface(chat_interface, css=css, description="Lily", clear_btn=None, undo_btn=None, retry_btn=None)
-    
-    # Add a button to save chat history
-    save_button = gr.Button("Save History")
-    save_button.click(fn=save_chat_history_to_salesforce)
+    # Add the chat interface only
+    chat = gr.ChatInterface(chat_interface, css=css, clear_btn=None, undo_btn=None, retry_btn=None)
 
 # Launch the Gradio interface
 demo.launch()