Create app.py

app.py

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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 math tutor providing hints and guidance. "
+    "Do not reveal final answers. Offer step-by-step assistance only."
+)
+
+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
+    )
+
+@spaces.GPU
+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 chat application helps with math problems by providing hints and guidance. "
+            "It keeps a history of your conversation and ensures no direct answers are given."
+        )
+
+        with gr.Row():
+            with gr.Column():
+                user_input = gr.Textbox(
+                    label="Your Math Query",
+                    placeholder="Ask about a math problem (e.g., Solve for x: 4x + 5 = 6x + 7)",
+                    lines=2
+                )
+                send_button = gr.Button("Send")
+            with gr.Column():
+                chat_history = gr.Textbox(
+                    label="Chat History",
+                    placeholder="Chat history will appear here.",
+                    lines=20,
+                    interactive=False
+                )
+
+        # Hidden state for storing conversation history
+        history_state = gr.State([])
+
+        # Button interaction
+        send_button.click(
+            fn=generate_response,
+            inputs=[history_state, user_input],
+            outputs=[chat_history, history_state]
+        )
+
+    return chat_app
+
+
+app = create_chat_interface()
+app.launch(debug=True)