Init repo.

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+export.pkl filter=lfs diff=lfs merge=lfs -text
+examples/forrest_1.png filter=lfs diff=lfs merge=lfs -text
+examples/forrest_2.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+examples/forrest_1.png
+examples/forrest_2.png
+examples/forrest_1.png
+examples/forrest_2.png

README.md

@@ -1,13 +1,13 @@
 ---
-title: Film Slate Or Nah
+title: Film Slate Or No Film Slate
 emoji: 👁
-colorFrom: blue
-colorTo: gray
+colorFrom: green
+colorTo: blue
 sdk: gradio
 sdk_version: 5.25.2
 app_file: app.py
 pinned: false
-short_description: Film slate, or nah?
+short_description: Classifies images as having or not having a film slate
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,141 @@
+from fastai.learner import load_learner
+from fastai.vision.core import PILImage
+from fastai.vision.all import DisplayedTransform
+from pathlib import Path
+import gradio as gr
+import PIL
+import sys
+import os
+import albumentations as A
+import numpy as np
+
+# Print debug information
+print(f"Python version: {sys.version}")
+print(f"PIL version: {PIL.__version__}")
+print(f"fastai path: {Path(__file__).parent}")
+print(f"Current working directory: {os.getcwd()}")
+
+# Create a custom transform class that integrates Albumentations with fastai
+class AlbumentationsTransform(DisplayedTransform):
+    split_idx, order = None, 2  # Apply to both train and validation sets
+    
+    def __init__(self, train_aug, valid_aug=None):
+        self.train_aug = train_aug
+        self.valid_aug = valid_aug or A.Compose([
+            A.SmallestMaxSize(max_size=224),
+            A.CenterCrop(height=224, width=224)
+        ])
+        
+    def before_call(self, b, split_idx):
+        self.split_idx = split_idx
+        
+    def encodes(self, img: PILImage):
+        # Use validation augmentation for inference
+        aug = self.valid_aug
+        # Convert to numpy array and ensure it's uint8 before augmentation
+        img_array = np.array(img).astype(np.uint8)
+        aug_img = aug(image=img_array)['image']
+        # Convert back to PIL Image
+        return PILImage.create(aug_img)
+
+# Define the albumentations transforms
+def get_train_transform():
+    return A.Compose([
+        A.SmallestMaxSize(max_size=256),
+        A.RandomCrop(height=224, width=224),
+        A.HorizontalFlip(p=0.5),
+        A.RandomBrightnessContrast(p=0.3),
+    ])
+
+def get_valid_transform():
+    return A.Compose([
+        A.SmallestMaxSize(max_size=256),
+        A.CenterCrop(height=224, width=224),
+    ])
+
+try:
+    learn = load_learner('export.pkl')
+    print(f"Model loaded successfully: {type(learn)}")
+    
+    # Define friendly label names
+    label_mapping = {
+        'positive': "Slate!",
+        'negative': "No Slate!"
+    }
+    
+    # Get original labels
+    original_labels = learn.dls.vocab
+    print(f"Original labels: {original_labels}")
+    
+    def predict(img):
+        # Convert to PILImage and apply transforms
+        img_pil = PILImage.create(img)
+        # Create transform with both train and valid transforms (will use valid for inference)
+        transform = AlbumentationsTransform(get_train_transform(), get_valid_transform())
+        transformed_img = transform.encodes(img_pil)
+        
+        # Get prediction
+        pred, pred_idx, probs = learn.predict(transformed_img)
+        
+        # Map the original labels to friendly names
+        return {label_mapping[original_labels[i]]: float(probs[i]) for i in range(len(original_labels))}
+
+    # Create a more attractive interface with custom styling
+    with gr.Blocks(css="footer {visibility: hidden}") as demo:
+        gr.Markdown("# 🎬 Image Classifier: Slate or No Slate?")
+        gr.Markdown("Upload an image and let AI tell you whether or not it contains a film slate!")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                # Remove webcam by setting sources to only 'upload' and 'clipboard'
+                input_image = gr.Image(
+                    type="pil",
+                    label="Upload Image",
+                    sources=["upload", "clipboard"]
+                )
+                submit_btn = gr.Button("Classify", variant="primary")
+            
+            with gr.Column(scale=1):
+                output = gr.Label(num_top_classes=3, label="Predictions")
+                
+        with gr.Accordion("About", open=False):
+            gr.Markdown("""
+            ## How it works
+            This app uses a deep learning model trained with fastai to classify images.
+            
+            ## Tips for best results
+            - Use clear, well-lit images
+            - Center the subject in the frame
+            - Supported categories: """ + ", ".join([label_mapping[label] for label in original_labels]))
+        
+        # Set up the prediction flow
+        submit_btn.click(
+            fn=predict,
+            inputs=input_image,
+            outputs=output
+        )
+        
+        # Allow image input to trigger prediction as well
+        input_image.change(
+            fn=predict,
+            inputs=input_image,
+            outputs=output
+        )
+        
+        # Add examples if you have them
+        gr.Examples(
+           examples=[
+               "examples/forrest_1.png",
+               "examples/forrest_2.png"
+           ],
+           inputs=input_image
+        )
+    
+    allowed_paths = ["examples/forrest_1.png", "examples/forrest_2.png"]
+
+    # Launch the app, allow access to the examples directory for @Gradio
+    demo.launch(allowed_paths=allowed_paths) # ssr_mode=False
+except Exception as e:
+    print(f"Error occurred: {e}")
+    import traceback
+    traceback.print_exc()

export.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9728965243f2f98129d46b636456c34aec3a946fe84b410731f702cdf10a8f3a
+size 114674957

requirements.txt

@@ -0,0 +1,4 @@
+albumentations==2.0.5
+fastai==2.7.18
+gradio==5.25.2
+timm==1.0.15