Upload 3 files

LR_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d152ffffdf42731a7ddb95c43975586b675db36966498bb4da0e70d5f7254652
+size 288854283

app.py

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+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()

requirement.txt

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+torch
+torchvision
+gradio
+Pillow