Srinivasulu kethanaboina commited on
Commit
4eb2710
1 Parent(s): 5751d9f

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +33 -6
app.py CHANGED
@@ -4,6 +4,7 @@ from dotenv import load_dotenv
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  from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
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  from llama_index.llms.huggingface import HuggingFaceInferenceAPI
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  from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 
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  import random
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  import datetime
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  import uuid
@@ -92,6 +93,26 @@ def save_chat_history(history):
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  json.dump(history, f)
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  print(f"Chat history saved as {chat_history_path}")
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  # Define the function to handle predictions
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  def predict(message, history):
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  logo_html = '''
@@ -107,7 +128,7 @@ def predict(message, history):
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  return response_with_logo
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- # Define your Gradio chat interface function (replace with your actual logic)
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  def chat_interface(message, history):
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  try:
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  # Process the user message and generate a response
@@ -151,8 +172,14 @@ div.svelte-rk35yg {display: none;}
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  div.progress-text.svelte-z7cif2.meta-text {display: none;}
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  '''
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- gr.ChatInterface(chat_interface,
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- css=css,
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- description="Lily",
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- clear_btn=None, undo_btn=None, retry_btn=None,
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- ).launch()
 
 
 
 
 
 
 
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  from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
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  from llama_index.llms.huggingface import HuggingFaceInferenceAPI
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  from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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+ from simple_salesforce import Salesforce, SalesforceLogin
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  import random
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  import datetime
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  import uuid
 
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  json.dump(history, f)
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  print(f"Chat history saved as {chat_history_path}")
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+ # Save to Salesforce
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+ save_to_salesforce(current_chat_history)
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+
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+ def save_to_salesforce(history):
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+ username =os.getenv("username")
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+ password =os.getenv("password")
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+ security_token =os.getenv("security_token")
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+ domain = 'test'
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+
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+ session_id, sf_instance = SalesforceLogin(username=username, password=password, security_token=security_token, domain=domain)
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+ sf = Salesforce(instance=sf_instance, session_id=session_id)
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+ for past_query, response in history:
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+ data = {
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+ 'Name': 'Chat with user',
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+ 'Bot_Message__c': response,
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+ 'User_Message__c': past_query,
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+ 'Date__c': str(datetime.datetime.now().date())
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+ }
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+ sf.Chat_History__c.create(data)
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+
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  # Define the function to handle predictions
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  def predict(message, history):
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  logo_html = '''
 
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  return response_with_logo
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+ # Define your Gradio chat interface function
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  def chat_interface(message, history):
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  try:
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  # Process the user message and generate a response
 
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  div.progress-text.svelte-z7cif2.meta-text {display: none;}
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  '''
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+ demo = gr.ChatInterface(chat_interface,
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+ css=css,
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+ description="Lily",
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+ clear_btn=None, undo_btn=None, retry_btn=None,
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+ )
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+
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+ # Add a button to save chat history
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+ gr.Button("Close Chat").click(fn=save_chat_history)
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+
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+ # Launch the interface
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+ demo.launch()