app.py

import subprocess
import sys
import os


def install_requirements():
    packages = [
        "numpy==1.24.3",
        "torch==2.0.1",
        "torchvision==0.15.2",
        "Pillow==9.5.0",
        "gradio==3.50.2"
    ]
    for package in packages:
        subprocess.check_call([sys.executable, "-m", "pip", "install", "--force-reinstall", package])


install_requirements()

import traceback
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image
import gradio as gr


class ModifiedLargeNet(nn.Module):
    def __init__(self):
        super(ModifiedLargeNet, self).__init__()
        self.name = "modified_large"
        self.conv1 = nn.Conv2d(3, 5, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(5, 10, 5)
        self.fc1 = nn.Linear(10 * 29 * 29, 32)
        self.fc2 = nn.Linear(32, 3)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 10 * 29 * 29)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x


try:
    model = ModifiedLargeNet()
    state_dict = torch.load("modified_large_net.pt", map_location=torch.device("cpu"))
    model.load_state_dict(state_dict)
    print("Model loaded successfully")
    model.eval()
except Exception as e:
    print(f"Error loading model: {str(e)}")
    traceback.print_exc()

transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(), 
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

def custom_transform(pil_image):
    np_image = np.array(pil_image)
    
    tensor_image = torch.from_numpy(np_image.transpose((2, 0, 1))).float()
    
    tensor_image = tensor_image / 255.0
    
    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
    tensor_image = normalize(tensor_image)
    
    return tensor_image

def process_image(image):
    if image is None:
        return None
    
    try:
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image.astype('uint8'))
        
        if image.mode != 'RGB':
            image = image.convert('RGB')
        
        image = image.resize((128, 128), Image.Resampling.LANCZOS)
        
        print(f"Processed image size: {image.size}")
        print(f"Processed image mode: {image.mode}")
        print(f"Image type: {type(image)}")
        
        return image
    except Exception as e:
        print(f"Error in process_image: {str(e)}")
        traceback.print_exc()
        return None

def predict(image):
    if image is None:
        return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}
    
    try:
        processed_image = process_image(image)
        if processed_image is None:
            return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}
        
        try:
            tensor_image = custom_transform(processed_image)
            tensor_image = tensor_image.unsqueeze(0) 
            
            print(f"Input tensor shape: {tensor_image.shape}")
            print(f"Tensor dtype: {tensor_image.dtype}")
            print(f"Tensor device: {tensor_image.device}")
            
        except Exception as e:
            print(f"Error in tensor conversion: {str(e)}")
            traceback.print_exc()
            return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}
        
        try:
            with torch.no_grad():
                outputs = model(tensor_image)
                print(f"Raw outputs: {outputs}")
                
                probabilities = F.softmax(outputs, dim=1)[0].cpu().numpy()
                print(f"Probabilities: {probabilities}")
            
            classes = ["Rope", "Hammer", "Other"]
            results = {cls: float(prob) for cls, prob in zip(classes, probabilities)}
            print(f"Final results: {results}")
            return results
            
        except Exception as e:
            print(f"Error in prediction: {str(e)}")
            traceback.print_exc()
            return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}
            
    except Exception as e:
        print(f"Prediction error: {str(e)}")
        traceback.print_exc()
        return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}

# Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"), 
    outputs=gr.Label(num_top_classes=3),
    title="Mechanical Tools Classifier",
    description="Upload an image of a tool to classify it as 'Rope', 'Hammer', or 'Other'.",
)

# Launch the interface
if __name__ == "__main__":
    interface.launch()