import gradio as gr
from transformers import pipeline
import torch
import numpy as np
from monitoring import PerformanceMonitor, measure_time

# Model IDs
MODEL_OPTIONS = {
    "Base Model": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
    "Fine-tuned Model": "Joash2024/Math-SmolLM2-1.7B"
}

# Initialize performance monitor
monitor = PerformanceMonitor()

def format_prompt(problem):
    """Format the input problem according to the model's expected format"""
    return f"Given a mathematical function, find its derivative.\n\nFunction: {problem}\nThe derivative of this function is:"

@measure_time
def get_model_response(problem, model_id):
    """Get response from a specific model"""
    try:
        # Initialize pipeline for each request
        pipe = pipeline(
            "text-generation",
            model=model_id,
            torch_dtype=torch.float16,
            device_map="auto",
            model_kwargs={"low_cpu_mem_usage": True}
        )
        
        # Format prompt and generate response
        prompt = format_prompt(problem)
        response = pipe(
            prompt,
            max_new_tokens=50,  # Shorter response
            temperature=0.1,
            do_sample=False,  # Deterministic
            num_return_sequences=1,
            return_full_text=False  # Only return new text
        )[0]["generated_text"]
        
        return response.strip()
    except Exception as e:
        return f"Error: {str(e)}"

def solve_problem(problem, problem_type, model_type):
    """Solve a math problem using the selected model"""
    if not problem:
        return "Please enter a problem", None
    
    # Record problem type
    monitor.record_problem_type(problem_type)
    
    # Add problem type context if provided
    if problem_type != "Custom":
        problem = f"{problem_type}: {problem}"
    
    # Get response from selected model
    model_id = MODEL_OPTIONS[model_type]
    response, time_taken = get_model_response(problem, model_id)
    
    # Format response with steps
    output = f"""Solution: {response}

Let's verify this step by step:
1. Starting with f(x) = {problem}
2. Applying differentiation rules
3. We get f'(x) = {response}"""
    
    # Record metrics
    monitor.record_response_time(model_type, time_taken)
    monitor.record_success(model_type, not response.startswith("Error"))
    
    # Get updated statistics
    stats = monitor.get_statistics()
    
    # Format statistics for display
    stats_display = f"""
### Performance Metrics

#### Response Times (seconds)
- {model_type}: {stats.get(f'{model_type}_avg_response_time', 0):.2f} avg

#### Success Rates
- {model_type}: {stats.get(f'{model_type}_success_rate', 0):.1f}%

#### Problem Types Used
"""
    for ptype, percentage in stats.get('problem_type_distribution', {}).items():
        stats_display += f"- {ptype}: {percentage:.1f}%\n"
    
    return output, stats_display

# Create Gradio interface
with gr.Blocks(title="Mathematics Problem Solver") as demo:
    gr.Markdown("# Mathematics Problem Solver")
    gr.Markdown("Test our models on mathematical problems")
    
    with gr.Row():
        with gr.Column():
            problem_type = gr.Dropdown(
                choices=["Addition", "Root Finding", "Derivative", "Custom"],
                value="Derivative",
                label="Problem Type"
            )
            model_type = gr.Dropdown(
                choices=list(MODEL_OPTIONS.keys()),
                value="Fine-tuned Model",
                label="Model to Use"
            )
            problem_input = gr.Textbox(
                label="Enter your math problem",
                placeholder="Example: x^2 + 3x"
            )
            solve_btn = gr.Button("Solve", variant="primary")
    
    with gr.Row():
        solution_output = gr.Textbox(label="Solution", lines=5)
    
    # Performance metrics display
    with gr.Row():
        metrics_display = gr.Markdown("### Performance Metrics\n*Solve a problem to see metrics*")
    
    # Example problems
    gr.Examples(
        examples=[
            ["x^2 + 3x", "Derivative", "Fine-tuned Model"],
            ["144", "Root Finding", "Fine-tuned Model"],
            ["235 + 567", "Addition", "Fine-tuned Model"],
            ["\\sin{\\left(x\\right)}", "Derivative", "Fine-tuned Model"],
            ["e^x", "Derivative", "Fine-tuned Model"],
            ["\\frac{1}{x}", "Derivative", "Fine-tuned Model"],
            ["x^3 + 2x", "Derivative", "Fine-tuned Model"],
            ["\\cos{\\left(x^2\\right)}", "Derivative", "Fine-tuned Model"]
        ],
        inputs=[problem_input, problem_type, model_type],
        outputs=[solution_output, metrics_display],
        fn=solve_problem,
        cache_examples=True,
    )
    
    # Connect the interface
    solve_btn.click(
        fn=solve_problem,
        inputs=[problem_input, problem_type, model_type],
        outputs=[solution_output, metrics_display]
    )

if __name__ == "__main__":
    demo.launch()