import gradio as gr import openai import os import json # OpenAI API setup openai.api_key = os.getenv("GROQ_API_KEY") openai.api_base = "https://api.groq.com/openai/v1" # File to store conversation history CONVERSATION_FILE = "conversation_history.json" # Function to load conversation history def load_history(): if not os.path.exists(CONVERSATION_FILE): # Create the file with an empty list as default content with open(CONVERSATION_FILE, "w") as file: json.dump([], file) try: with open(CONVERSATION_FILE, "r") as file: return json.load(file) except json.JSONDecodeError: return [] # Function to save conversation history def save_history(history): try: with open(CONVERSATION_FILE, "w") as file: json.dump(history, file, indent=4) except Exception as e: print(f"Error saving history: {e}") # Function to clear conversation history def clear_conversation_history(): try: with open(CONVERSATION_FILE, "w") as file: json.dump([], file) return "Conversation history cleared successfully.", "" except Exception as e: return f"Error clearing history: {e}", "" # Function to format bot response def format_bot_response(response): """ Converts markdown-like symbols to HTML and structures response. """ response = response.replace("**", "").replace("**", "") # Bold formatting response = response.replace("1.", "
  1.") response = response.replace("2.", "
  2.") response = response.replace("3.", "
  3.") response = response.replace("4.", "
  4.") response = response.replace("5.", "
  5.") return f"
{response}
" # Function to get response from the LLM def get_groq_response(message, history=[]): try: messages = [{"role": "system", "content": "Precise answer"}] + history + [{"role": "user", "content": message}] response = openai.ChatCompletion.create( model="llama-3.1-70b-versatile", messages=messages ) return format_bot_response(response.choices[0].message["content"]) except Exception as e: return f"Error: {str(e)}" # Chatbot function def chatbot(user_input, history): # Load conversation history conversation_history = history or load_history() # Format history for the LLM formatted_history = [{"role": "user" if i % 2 == 0 else "assistant", "content": msg} for i, (msg, _) in enumerate(conversation_history)] + \ [{"role": "assistant", "content": response} for _, response in conversation_history] # Get bot response bot_response = get_groq_response(user_input, formatted_history) # Update history with the new conversation conversation_history.append((user_input, bot_response)) # Save the updated history save_history(conversation_history) # Format for HTML display display_html = "".join( f"
User: {user}
" f"
Bot: {bot}
" for user, bot in conversation_history ) return conversation_history, display_html, "" # Clear the user input field # Gradio Interface with gr.Blocks(css=""" .user-message { background-color: #9ACBD0; padding: 10px; margin: 10px; border-radius: 8px; max-width: 60%; float: right; clear: both; } .bot-message { background-color: #F2EFE7; padding: 10px; margin: 10px; border-radius: 8px; max-width: 60%; float: left; clear: both; } .user-message:hover, .bot-message:hover { transform: scale(1.02); box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1); } """) as demo: gr.Markdown("""# Mom: We have ChatGPT at Home, \n ChatGPT at Home: """) chat_display = gr.HTML(label="Conversation") user_input = gr.Textbox(label="Type your message here: Feel free to ask questions. After you're done, remember to clear the history for privacy. ") clear_button = gr.Button("Clear History") system_message = gr.Textbox(label="System Message", interactive=False) history_state = gr.State(load_history()) # Chat interaction user_input.submit(chatbot, inputs=[user_input, history_state], outputs=[history_state, chat_display, user_input]) # Clear history button action clear_button.click(clear_conversation_history, inputs=None, outputs=[system_message, chat_display]) clear_button.click(lambda: [], outputs=history_state) # Reset the history state # Launch the app demo.launch()