Update app.py

app.py

@@ -1,36 +1,37 @@
-from pathlib import Path
-from fastai.vision.all import *
-import gradio as gr
-
-examples = [
-    ["project/WBC-Benign-017.jpg"],  # Replace with the actual paths to your images
-    ["project/WBC-Benign-030.jpg"],
-    ["project/WBC-Malignant-Early-027.jpg"],
-    ["project/WBC-Malignant-Pre-019.jpg"],
-    ["project/WBC-Malignant-Pro-027.jpg"]
-]
-# Correctly format the path for Windows
-model_path = Path(r'efficientnet_b3_model.pkl')
-
-# Load the model
-learn = load_learner(model_path, cpu=True)
-
-# Define the prediction function
-def classify_image(image):
-    pred, idx, probs = learn.predict(image)
-    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}
-
-# Set up the Gradio interface
-interface = gr.Interface(
-    fn=classify_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=3),
-    title="EfficientNet B3 Image Classifier",
-    examples= examples,
-    description="Upload an image to classify using the trained EfficientNet B3 model.",
-)
-
-
-# Launch the app
-if __name__ == "__main__":
-    interface.launch(share=True)
+from pathlib import Path
+from fastai.vision.all import *
+import gradio as gr
+
+examples = [
+    ["project/WBC-Benign-017.jpg"],  # Replace with the actual paths to your images
+    ["project/WBC-Benign-030.jpg"],
+    ["project/WBC-Malignant-Early-027.jpg"],
+    ["project/WBC-Malignant-Pre-019.jpg"],
+    ["project/WBC-Malignant-Pro-027.jpg"]
+]
+# Correctly format the path for Windows
+model_path = Path(r'efficientnet_b3_model.pkl')
+
+# Load the model
+learn = load_learner(model_path, cpu=True)
+
+# Define the prediction function
+def classify_image(image):
+    pred, idx, probs = learn.predict(image)
+    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Label(num_top_classes=3), gr.Textbox(label="Image Name")],
+    title="EfficientNet B3 Image Classifier",
+    examples= examples,
+    description="Upload an image to classify using the trained EfficientNet B3 model.",
+    
+)
+
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch(share=True)