Initial commit with app.py, .gitignore, and requirements.txt

.gitignore

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+# Python cache and bytecode
+__pycache__/
+*.py[cod]
+*.so
+
+# Virtual environments
+env/
+venv/
+.venv/
+
+# Model weights and large files
+*.pth
+*.pt
+
+# Output images and plots
+*.png
+*.jpg
+*.jpeg
+
+# Gradio cached files
+gradio_cached_examples/
+
+# System/OS files
+.DS_Store
+Thumbs.db
+
+# VS Code settings
+.vscode/
+.idea/

app.py

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+import gradio as gr
+import torch
+from torchvision import transforms
+from PIL import Image
+from torchvision.transforms import InterpolationMode
+from torchvision.models import efficientnet_b3
+
+# Model setup
+class_names = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
+model = efficientnet_b3(weights=None)
+model.classifier[1] = torch.nn.Linear(in_features=1536, out_features=len(class_names))
+
+model.load_state_dict(torch.load(
+    "Eff_net_b3_01_brain_tumor.pth",
+    map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+))
+model.eval()
+
+# Image transform
+img_transform = transforms.Compose([
+    transforms.Resize(320, interpolation=InterpolationMode.BICUBIC),
+    transforms.CenterCrop(300),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+# Prediction function
+def predict(image):
+    transformed_image = img_transform(image).unsqueeze(0)
+    with torch.inference_mode():
+        preds = model(transformed_image)
+        probs = torch.softmax(preds, dim=1)
+        label_idx = torch.argmax(probs, dim=1).item()
+        class_label = class_names[label_idx]
+        confidence = probs[0, label_idx].item()
+    return class_label, confidence
+
+# Gradio Blocks UI
+with gr.Blocks(title="🧠 Brain Tumor MRI Classifier") as demo:
+    gr.Markdown("## 🧠 Brain Tumor Classifier (EfficientNet-B3)")
+    gr.Markdown("""
+    Upload an MRI scan of the brain, and this model will classify it as one of:
+    - **Glioma Tumor**
+    - **Meningioma Tumor**
+    - **Pituitary Tumor**
+    - **No Tumor**
+
+    Uses EfficientNet-B3 trained on labeled brain MRI dataset.
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload MRI Image")
+            predict_button = gr.Button("🔍 Predict")
+            clear_button = gr.Button("🧹 Clear")
+
+        with gr.Column():
+            output_label = gr.Label(label="Predicted Class")
+            confidence_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="Confidence Score")
+
+    predict_button.click(fn=predict, inputs=image_input, outputs=[output_label, confidence_slider])
+    clear_button.click(lambda: (None, None), inputs=[], outputs=[image_input, output_label, confidence_slider])
+
+    gr.Markdown("---")
+    gr.Markdown(
+        "<center>👤 Developed by [Sagar Bisht](https://www.linkedin.com/in/sagarbisht123)</center>",
+        elem_id="footer"
+    )
+
+demo.launch(share=True)

requirements.txt

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+torch==1.12.1+cu113
+torchvision==0.13.1+cu113
+Pillow==10.4.0
+gradio==3.4.0
+numpy==1.24.3
+python==3.9.21
+tqdm==4.67.1
+matplotlib==3.9.4