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best_model (2).pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:919069e965c757cefdc54331e2e95e2268ea588ac8c01e302366b94c2c0814a2
+size 98551872

covid.py

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+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import torchvision.models as models
+import torch.nn as nn
+
+# 🔹 Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 🔹 Load ResNet50 and modify for 2-class output
+model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
+in_features = model.fc.in_features
+model.fc = nn.Sequential(
+    nn.Linear(in_features, 512),
+    nn.ReLU(),
+    nn.Dropout(0.4),
+    nn.Linear(512, 2)  # 2 classes: NORMAL, PNEUMONIA
+)
+
+# 🔹 Load the trained model
+model.load_state_dict(torch.load("best_model (2).pth", map_location=device))
+model.to(device)
+model.eval()
+
+# 🔹 Preprocessing - exactly matching your val_transforms
+transform = transforms.Compose([
+    transforms.Lambda(lambda img: img.convert("RGB")),  # 🧠 Force RGB
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5]*3, std=[0.5]*3)
+])
+
+# 🔹 Class labels
+class_names = ["NORMAL", "PNEUMONIA"]
+
+# 🔹 Inference function
+def classify_image(img):
+    img = transform(img).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model(img)
+        probs = torch.nn.functional.softmax(outputs, dim=1)
+        return {class_names[i]: float(probs[0][i]) for i in range(len(class_names))}
+
+# 🔹 Gradio Interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2),
+    title="🩺 Pneumonia Classifier",
+    description="Upload a chest X-ray image. The model predicts whether it's NORMAL or shows signs of PNEUMONIA."
+)
+
+# 🔹 Launch the app
+interface.launch()

requirements.txt

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+torch
+torchvision
+gradio
+pillow