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| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import gradio as gr | |
| import torch | |
| import spaces | |
| # Initialize the model and tokenizer | |
| model_name = "Qwen/Qwen2.5-Math-1.5B-Instruct" | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| torch_dtype=torch.float16 if device == "cuda" else torch.float32, | |
| device_map="auto" if device == "cuda" else None | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # System instruction | |
| SYSTEM_INSTRUCTION = ( | |
| "You are a helpful and patient math tutor tasked with providing step-by-step hints and guidance for solving math problems." | |
| "Your primary role is to assist learners in understanding how to approach and solve problems without revealing the final answer, even if explicitly requested." | |
| "Always encourage the learner to solve the problem themselves by offering incremental hints and explanations." | |
| "Under no circumstances should you provide the complete solution or final answer." | |
| ) | |
| def apply_chat_template(messages): | |
| """ | |
| Prepares the messages for the model using the tokenizer's chat template. | |
| """ | |
| return tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| def generate_response(history, user_input): | |
| """ | |
| Generates a response from the model based on the chat history and user input. | |
| """ | |
| # Append user input to the chat history | |
| history.append({"role": "user", "content": user_input}) | |
| # Build messages for the model | |
| messages = [{"role": "system", "content": SYSTEM_INSTRUCTION}] + history | |
| # Tokenize input for the model | |
| text = apply_chat_template(messages) | |
| model_inputs = tokenizer([text], return_tensors="pt").to(device) | |
| # Generate response | |
| generated_ids = model.generate( | |
| **model_inputs, | |
| max_new_tokens=512 | |
| ) | |
| generated_ids = [ | |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) | |
| ] | |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| # Append the assistant's response to history | |
| history.append({"role": "assistant", "content": response}) | |
| # Format the conversation for display | |
| formatted_history = format_chat_history(history) | |
| return formatted_history, history | |
| def format_chat_history(history): | |
| """ | |
| Formats the conversation history for a user-friendly chat display. | |
| """ | |
| chat_display = "" | |
| for message in history: | |
| if message["role"] == "user": | |
| chat_display += f"**User:** {message['content']}\n\n" | |
| elif message["role"] == "assistant": | |
| chat_display += f"**MathTutor:** {message['content']}\n\n" | |
| return chat_display | |
| # Gradio chat interface | |
| def create_chat_interface(): | |
| """ | |
| Creates the Gradio interface for the chat application. | |
| """ | |
| with gr.Blocks() as chat_app: | |
| gr.Markdown("## Math Hint Chat") | |
| gr.Markdown( | |
| "This chatbot provides hints and step-by-step guidance for solving math problems. " | |
| "It will not reveal the final answer." | |
| ) | |
| chatbot = gr.Chatbot(label="Math Tutor Chat") | |
| user_input = gr.Textbox( | |
| placeholder="Ask your math question here (e.g., Solve for x: 4x + 5 = 6x + 7)", | |
| label="Your Query" | |
| ) | |
| send_button = gr.Button("Send") | |
| # Hidden state for managing chat history | |
| chat_history = gr.State([]) | |
| # Button interaction for chat | |
| send_button.click( | |
| fn=generate_response, | |
| inputs=[chat_history, user_input], | |
| outputs=[chatbot] | |
| ) | |
| return chat_app | |
| app = create_chat_interface() | |
| app.launch(debug=True) |