Update app.py

app.py

@@ -1,37 +1,36 @@
-__all__ = ['learn', 'classify_image', 'categories', 'classifier', 'virtual','image', 'label', 'examples', 'intf']
+__all__ = [
+    'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf'
+]
 
-# Cell
 from fastai.vision.all import *
 import gradio as gr
-import timm
-import pickle
-import torch
+import timm  # keep only if your model actually needs timm
 
-#   with open('./model.pkl', 'rb') as f:
-#    model = pickle.load(f)
+# ✅ Load the fastai model properly — no manual pickle.load()
+learn = load_learner('model.pkl', cpu=True)
 
-with open('model.pkl', 'rb') as f:
-    model = pickle.load(f)
-
-def is_real(x): return x[0].isupper()
-
-#|export
-learn = load_learner('model.pkl')  
-
-#|export
-categories =('Virtual Staging','Real')
+# Define your categories exactly as trained
+categories = ('Virtual Staging', 'Real')
 
+# Prediction function for Gradio
 def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories,map(float,probs)))
+    pred, idx, probs = learn.predict(img)
+    # Cast to float so Gradio handles them cleanly
+    return dict(zip(categories, map(float, probs)))
 
-#*** We have to cast to float above because KAGGLE does not return number on the answer it returns tensors, and Gradio does not deal with numpy so we have to cast to float
-
-#|export
-image = gr.inputs.Image(shape=(192,192))
+# Gradio UI components
+image = gr.inputs.Image(shape=(192, 192))
 label = gr.outputs.Label()
-examples = ['virtual.jpg','real.jpg']
-
-# Cell
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples,share=True)
-intf.launch()
+examples = ['virtual.jpg', 'real.jpg']  # sample files in your Space
+
+# Create and launch interface
+intf = gr.Interface(
+    fn=classify_image,
+    inputs=image,
+    outputs=label,
+    examples=examples,
+    share=True
+)
+
+if __name__ == "__main__":
+    intf.launch()