Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import requests | |
| from huggingface_hub import InferenceClient | |
| # Initialize Hugging Face client | |
| HF_MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| HF_TOKEN = "your_hugging_face_api_token" # Replace with your token | |
| client = InferenceClient(model=HF_MODEL, token=HF_TOKEN) | |
| # Persistent bot knowledge state | |
| bot_knowledge = {"dataset": None} | |
| # Train chatbot by setting the dataset | |
| def train_chatbot(dataset): | |
| bot_knowledge["dataset"] = dataset | |
| return "Chatbot trained successfully!" | |
| # Chat function to process user input and generate bot responses | |
| def chat_with_bot(history, user_input): | |
| if not bot_knowledge["dataset"]: | |
| return history + [{"role": "bot", "content": "No dataset loaded. Please train the bot first."}] | |
| # Append user input to the chat history | |
| history.append({"role": "user", "content": user_input}) | |
| # Generate bot response | |
| prompt = f"{bot_knowledge['dataset']} {user_input}" | |
| try: | |
| response = client.text_generation(prompt=prompt, max_new_tokens=128) | |
| bot_response = response.get("generated_text", "Sorry, I couldn't generate a response.") | |
| except Exception as e: | |
| bot_response = f"Error generating response: {e}" | |
| # Append bot response to the history | |
| history.append({"role": "bot", "content": bot_response}) | |
| return history | |
| # Gradio Interface | |
| with gr.Blocks(theme="default") as app: | |
| gr.Markdown("# **Intelligent Chatbot with Knowledge Training**") | |
| gr.Markdown( | |
| """ | |
| Train a chatbot with custom datasets and interact with it dynamically. | |
| The bot will persist knowledge from the dataset and answer questions accordingly. | |
| """ | |
| ) | |
| # Train chatbot section | |
| with gr.Row(): | |
| chat_dataset = gr.Textbox( | |
| label="Dataset for Training", | |
| placeholder="Paste a dataset here to train the chatbot.", | |
| lines=5, | |
| ) | |
| train_button = gr.Button("Train Chatbot") | |
| train_status = gr.Textbox(label="Training Status", interactive=False) | |
| # Chat section | |
| with gr.Row(): | |
| chatbot = gr.Chatbot( | |
| label="Chat with Trained Bot", | |
| type="messages", | |
| avatar_user="https://example.com/user-avatar.png", | |
| avatar_bot="https://example.com/bot-avatar.png", | |
| ) | |
| user_input = gr.Textbox( | |
| label="Your Message", | |
| placeholder="Type your message and press Enter...", | |
| lines=1, | |
| ) | |
| # Train chatbot logic | |
| train_button.click(train_chatbot, inputs=[chat_dataset], outputs=[train_status]) | |
| # Chat interaction logic | |
| user_input.submit(chat_with_bot, inputs=[chatbot, user_input], outputs=chatbot) | |
| # Launch app | |
| if __name__ == "__main__": | |
| app.launch(server_name="0.0.0.0", server_port=7860) | |