app.py

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
import gradio as gr

def load_model(path="LR_model.pth"):
    model = models.resnet50(weights=None)

    # Your saved model has a Sequential head, not just one linear layer
    model.fc = nn.Sequential(
        nn.Linear(model.fc.in_features, 256),
        nn.ReLU(),
        nn.Dropout(0.4),
        nn.Linear(256, 2)
    )

    checkpoint = torch.load(path, map_location="cpu")
    model.load_state_dict(checkpoint["model_state_dict"])
    model.eval()
    return model



# Image preprocessing
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.4326, 0.4953, 0.3120], [0.2178, 0.2214, 0.2091])
])

# Predict function
def predict(img):
    img = img.convert("RGB")
    tensor = transform(img).unsqueeze(0)
    with torch.no_grad():
        output = model(tensor)
        probs = torch.nn.functional.softmax(output, dim=1)
        idx = probs.argmax().item()
        conf = probs[0][idx].item()
    return {"Parasitized" if idx == 0 else "Uninfected": conf}

# Load model once
model = load_model()

# Gradio UI
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=2),
    title=" Malaria Cell Detection",
    description="Upload a blood smear cell image to check for malaria (parasitized or uninfected)."
)

interface.launch()