import gradio as gr import random import time from typing import List, Tuple # Mock AI responses for demonstration def get_ai_response(user_message: str, chat_history: List[Tuple[str, str]]) -> Tuple[str, List[Tuple[str, str]]]: """ Simulates an AI chatbot response. In a real application, you would connect this to an actual AI model. """ # Simple pattern matching for demonstration user_message_lower = user_message.lower() if "hello" in user_message_lower or "hi" in user_message_lower: responses = [ "Hello there! How can I assist you today?", "Hi! Nice to meet you!", "Greetings! What can I help you with today?" ] elif "how are you" in user_message_lower: responses = [ "I'm doing great! Just here ready to chat with you.", "I'm functioning perfectly! How about you?", "All systems operational! What's on your mind?" ] elif "weather" in user_message_lower: responses = [ "I don't have real-time weather data, but I hope it's pleasant wherever you are!", "I'm an AI, so I don't experience weather, but I can discuss it with you!" ] elif "help" in user_message_lower: responses = [ "I'm here to help! You can ask me about various topics, or we can just have a friendly conversation!" ] elif "bye" in user_message_lower or "goodbye" in user_message_lower: responses = [ "Goodbye! It was nice chatting with you!", "Farewell! Hope to talk with you again soon!", "See you later! Don't hesitate to come back if you have more questions!" ] else: responses = [ "That's interesting! Tell me more about that.", "I appreciate you sharing that with me.", "That's fascinating! I'd love to hear more." ] # Add some randomness and thinking simulation thinking_time = random.uniform(0.5, 2.0) time.sleep(thinking_time) return random.choice(responses), chat_history + [(user_message, random.choice(responses))] def stream_ai_response(user_message: str, chat_history: List[Tuple[str, str]]): """ Streams AI responses character by character for a more natural feel. """ full_response, chat_history = get_ai_response(user_message, chat_history) for i in range(len(full_response)): partial_response = full_response[:i+1] yield partial_response, chat_history + [(user_message, partial_response)] def handle_user_input(user_message: str, chat_history: List[Tuple[str, str]]): """Process user input and update chat history.""" if not user_message.strip(): return "", chat_history # Get AI response ai_response, chat_history = get_ai_response(user_message, chat_history) return "", chat_history def clear_chat(): """Clear the chat history.""" return [], [] def like_message(): """Handle message liking.""" gr.Info("Thanks for the feedback!") def retry_message(): """Handle message retry.""" gr.Warning("Retrying the last response...") # Create custom theme for the chatbot custom_theme = gr.themes.Soft( primary_hue="indigo", secondary_hue="blue", neutral_hue="slate", font=gr.themes.GoogleFont("Inter"), text_size="lg", spacing_size="lg", radius_size="md" ).set( button_primary_background_fill="*primary_600", button_primary_background_fill_hover="*primary_700", block_title_text_weight="600", ) with gr.Blocks() as demo: # Header with title and anycoder link with gr.Row(): gr.Markdown("# 🤖 AI Chatbot") gr.HTML( '
' '' 'Built with anycoder' '' '
' ) # Description gr.Markdown( "Welcome to your AI assistant! I'm here to help with questions, " "have conversations, or just chat about whatever's on your mind." ) # Chat interface with gr.Row(): with gr.Column(scale=3): chatbot = gr.Chatbot( label="Chat", height=500, show_copy_button=True, show_share_button=True, placeholder="Type your message here...", show_clear_button=True, ) with gr.Column(scale=2): # Input controls with gr.Group(): user_input = gr.Textbox( label="Your Message", placeholder="Type your message here and press Enter...", lines=2, max_lines=5, ) with gr.Row(): send_btn = gr.Button("Send", variant="primary") clear_btn = gr.Button("Clear Chat", variant="secondary") # Additional controls with gr.Accordion("Advanced Options", open=False): with gr.Row(): like_btn = gr.Button("👍 Like", size="sm") retry_btn = gr.Button("🔄 Retry", size="sm") # Status indicators with gr.Row(): gr.Markdown("**Status:** Ready") # Event handling send_btn.click( fn=handle_user_input, inputs=[user_input, chatbot], outputs=[user_input, chatbot], api_visibility="public" ) user_input.submit( fn=handle_user_input, inputs=[user_input, chatbot], outputs=[user_input, chatbot], api_visibility="public" ) clear_btn.click( fn=clear_chat, inputs=None, outputs=[chatbot, user_input], api_visibility="public" ) like_btn.click( fn=like_message, inputs=None, outputs=None, api_visibility="private" ) retry_btn.click( fn=retry_message, inputs=None, outputs=None, api_visibility="private" ) # Streaming example with gr.Group(visible=False) as streaming_section: gr.Markdown("### Streaming Response Demo") streaming_input = gr.Textbox(label="Streaming test message") streaming_output = gr.Textbox(label="Streaming response", interactive=False) # Examples gr.Examples( examples=[ "Hello, how are you today?", "What's the weather like?", "Can you help me with something?", "Tell me a fun fact!", "What can you do as an AI assistant?" ], inputs=user_input ) # Launch the application with modern Gradio 6 syntax demo.launch( theme=custom_theme, footer_links=[ {"label": "API Documentation", "url": "/docs"}, {"label": "About", "url": "/about"} ], share=False, # Set to True for public sharing show_error=True, )