app.py

from fastapi import FastAPI, Request, HTTPException, WebSocket, WebSocketDisconnect
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles
from fastapi.responses import HTMLResponse
from pydantic import BaseModel
from typing import List, Optional
import uvicorn
import torch
from scripts.model import Net
from scripts.training.train import train
import json
import os
from pathlib import Path
import asyncio
from fastapi import BackgroundTasks

app = FastAPI()

# Mount static files and templates
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")

# Model configurations
class TrainingConfig(BaseModel):
    block1: int
    block2: int
    block3: int
    optimizer: str
    batch_size: int
    epochs: int = 1

class ComparisonConfig(BaseModel):
    model1: TrainingConfig
    model2: TrainingConfig

def get_available_models():
    models_dir = Path("scripts/training/models")
    if not models_dir.exists():
        models_dir.mkdir(exist_ok=True, parents=True)
    return [f.stem for f in models_dir.glob("*.pth")]

# Add a global variable to store training task
training_task = None

@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.get("/train", response_class=HTMLResponse)
async def train_page(request: Request):
    return templates.TemplateResponse("train.html", {"request": request})

@app.get("/inference", response_class=HTMLResponse)
async def inference_page(request: Request):
    available_models = get_available_models()
    return templates.TemplateResponse(
        "inference.html", 
        {
            "request": request,
            "available_models": available_models
        }
    )

@app.post("/train")
async def train_model(config: TrainingConfig, background_tasks: BackgroundTasks):
    try:
        # Create model instance with the configuration
        model = Net(
            kernels=[config.block1, config.block2, config.block3]
        )
        
        # Store training configuration
        training_config = {
            "optimizer": config.optimizer,
            "batch_size": config.batch_size
        }
        
        return {"status": "success", "message": "Training configuration received"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.websocket("/ws/train")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    try:
        # Wait for configuration from client
        config_data = await websocket.receive_json()
        
        # Create model instance with the configuration
        model = Net(
            kernels=[
                config_data['block1'],
                config_data['block2'],
                config_data['block3']
            ]
        )
        
        # Create config object
        from scripts.training.config import NetworkConfig
        config = NetworkConfig()
        config.update(
            block1=config_data['block1'],
            block2=config_data['block2'],
            block3=config_data['block3'],
            optimizer=config_data['optimizer'],
            batch_size=config_data['batch_size'],
            epochs=1
        )
        
        print(f"Starting training with config: {config_data}")
        
        # Start training with websocket for real-time updates
        try:
            await train(model, config, websocket)
            await websocket.send_json({
                "type": "training_complete",
                "data": {
                    "message": "Training completed successfully!"
                }
            })
        except Exception as e:
            print(f"Training error: {str(e)}")
            await websocket.send_json({
                "type": "training_error",
                "data": {
                    "message": f"Training failed: {str(e)}"
                }
            })
            
    except WebSocketDisconnect:
        print("WebSocket disconnected")
    except Exception as e:
        print(f"WebSocket error: {str(e)}")
    finally:
        print("WebSocket connection closed")

# @app.post("/api/train_single")
# async def train_single_model(config: TrainingConfig):
#     try:
#         model = Net(kernels=config.kernels)
#         # Start training without passing the websocket
#         await train(model, config)
#         return {"status": "success"}
#     except Exception as e:
#         # Log the error for debugging
#         print(f"Error during training: {str(e)}")
#         # Return a JSON response with the error message
#         raise HTTPException(status_code=500, detail=f"Error during training: {str(e)}")

@app.post("/api/train_compare")
async def train_compare_models(config: ComparisonConfig):
    try:
        # Train both models
        model1 = Net(kernels=config.model1.kernels)
        model2 = Net(kernels=config.model2.kernels)
        
        results1 = train(model1, config.model1)
        results2 = train(model2, config.model2)
        
        return {
            "status": "success",
            "model1_results": results1,
            "model2_results": results2
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/api/inference")
async def perform_inference(data: dict):
    try:
        model_name = data.get("model_name")
        if not model_name:
            raise HTTPException(status_code=400, detail="No model selected")
            
        model_path = Path("scripts/training/models") / f"{model_name}.pth"
        if not model_path.exists():
            raise HTTPException(status_code=404, detail=f"Model not found: {model_path}")
            
        # Load model and perform inference
        model = Net()
        model.load_state_dict(torch.load(str(model_path), map_location=torch.device('cpu')))
        model.eval()
        
        # Process image data and get prediction
        image_data = data.get("image")
        if not image_data:
            raise HTTPException(status_code=400, detail="No image data provided")
            
        # Convert base64 image to tensor and process
        try:
            # Remove the data URL prefix
            image_data = image_data.split(',')[1]
            import base64
            import io
            from PIL import Image
            import torchvision.transforms as transforms
            
            # Decode base64 to image
            image_bytes = base64.b64decode(image_data)
            image = Image.open(io.BytesIO(image_bytes)).convert('L')  # Convert to grayscale
            
            # Resize using PIL directly with LANCZOS
            image = image.resize((28, 28), Image.LANCZOS)
            
            # Preprocess image
            transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.1307,), (0.3081,))
            ])
            
            # Convert to tensor and add batch dimension
            image_tensor = transform(image).unsqueeze(0)
            
            # Get prediction
            with torch.no_grad():
                output = model(image_tensor)
                prediction = output.argmax(dim=1).item()
            
            return {"prediction": prediction}
            
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
            
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/train/single", response_class=HTMLResponse)
async def train_single_page(request: Request):
    return templates.TemplateResponse("train_single.html", {"request": request})

@app.get("/train/compare", response_class=HTMLResponse)
async def train_compare_page(request: Request):
    return templates.TemplateResponse("train_compare.html", {"request": request})

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)