Create app.py

app.py

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+import os  # For reading environment variables
+import shutil  # For directory cleanup
+import zipfile  # For extracting model archives
+import pathlib  # For path manipulations
+import tempfile  # For creating temporary files/directories
+
+import gradio  # For interactive UI
+import pandas  # For tabular data handling
+import PIL.Image # For image I/O
+
+import huggingface_hub # For downloading model assets
+import autogluon.multimodal  # For loading AutoGluon image classifier
+
+# Hardcoded Hub model (native zip)
+MODEL_REPO_ID = "apsora/autoML_images_data"
+ZIP_FILENAME  = "autogluon_image_predictor_dir.zip"
+
+# Local cache/extract dirs
+CACHE_DIR   = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+
+# Download & load the native predictor
+def _prepare_predictor_dir() -> str: 
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    local_zip = huggingface_hub.hf_hub_download(
+        repo_id=MODEL_REPO_ID,
+        filename=ZIP_FILENAME,
+        repo_type="model",
+        local_dir=str(CACHE_DIR),
+        local_dir_use_symlinks=False,
+    )
+    if EXTRACT_DIR.exists():
+        shutil.rmtree(EXTRACT_DIR)
+    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
+    with zipfile.ZipFile(local_zip, "r") as zf:
+        zf.extractall(str(EXTRACT_DIR))
+    contents = list(EXTRACT_DIR.iterdir())
+    predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else EXTRACT_DIR
+    return str(predictor_root)
+
+PREDICTOR_DIR = _prepare_predictor_dir()
+PREDICTOR = autogluon.multimodal.MultiModalPredictor.load(PREDICTOR_DIR)  
+
+# Explicit class labels (edit copy as desired)
+CLASS_LABELS = {0: "no_tomato", 1: "tomato"}  
+
+# Helper to map model class -> human label
+def _human_label(c): 
+    try:
+        ci = int(c)
+        return CLASS_LABELS.get(ci, str(c))
+    except Exception:
+        return CLASS_LABELS.get(c, str(c))
+
+# Do the prediction!
+def do_predict(pil_img: PIL.Image.Image):
+    # Make sure there's actually an image to work with
+    if pil_img is None:
+        return "No image provided.", {}, pandas.DataFrame(columns=["Predicted label", "Confidence (%)"])
+
+    # IF we have something to work with, save it and prepare the input
+    tmpdir = pathlib.Path(tempfile.mkdtemp()) 
+    img_path = tmpdir / "input.png" 
+    pil_img.save(img_path)
+
+    df = pandas.DataFrame({"image": [str(img_path)]})  # For AutoGluon expected input format
+
+    # For class probabilities
+    proba_df = PREDICTOR.predict_proba(df)  
+
+    # For user-friendly column names
+    proba_df = proba_df.rename(columns={0: "no_tomato", 1: "tomato"})
+    row = proba_df.iloc[0]
+
+    # For pretty ranked dict expected by gr.Label
+    pretty_dict = {
+        "no_tomato": float(row.get("♻️ Recycling (0)", 0.0)),
+        "tomato": float(row.get("🗑️ Trash (1)", 0.0)),
+    }
+
+    return pretty_dict
+
+# Representative example images! These can be local or links.
+EXAMPLES = [
+    ["https://datasets-server.huggingface.co/assets/Iris314/Food_tomatoes_dataset/--/bcca6b7b9e4be8378efc59e98a95f1c44a5055ae/--/default/original/1/image/image.jpg?Expires=1758728666&Signature=lGRInfYT2bhH2zr~T-oKF6vTjts~uJz7rQFTF4eWoP2Yz-XzKQz2hpB-83pGTQm7k-TLKbgTwbKUHxoliG0V140W0YPMhlW-HQ6LBLAszEeSNwzwzaTNzQIso4bKfCy9KUBBMTFqRfbPA8QNw3Z~M4BiHS~7otx1SavJa-L53FbREbA4VZKanGNwyd0KSt6e~FfRZDgsKwbtEVoFuFQjID0q3GwBMLUXMQCk3gXCXftCiw5x1Et1EldmNEJsK38MA7ohKLcbr130NKyH0lo960QBcuVIkvhi7obweDKucGmRMUidmIPALl9I5ak8rAAy~jRXEr-nPR-c28foVUsMcw__&Key-Pair-Id=K3EI6M078Z3AC3"], 
+    ["https://datasets-server.huggingface.co/assets/Iris314/Food_tomatoes_dataset/--/bcca6b7b9e4be8378efc59e98a95f1c44a5055ae/--/default/original/10/image/image.jpg?Expires=1758728666&Signature=bUO6jZnrnaGh2mflzrFtRwO~SXRr5G-Sy3Tdo1~W1oPS8yYBPJAMKGrOdii7~Y71g8XihQVtFecpyawyt0RAh2UKrdczFockU3vgBsA6YujYCqZkSYC0KFSM--wO5pGtGn1AHJcBw48MZpdfSeUbxJoZy6z5YecOYrtD6Rz7eUhaghrT1S-lIwqW~gOqyRy8Ue-3OC5DtkBtuKq5LCkXXtssfDBGB8qFOjG1Vba68Gi9XfLXyBashf7tFTgNXjl3iXFjDtEkuo17siZvh1dK~ImfNbKVcBVv1vfqUC2lQWl7Jc4jaK4h2lYMFuevCfaurwELOUZVt2I8KIb0FWdjMQ__&Key-Pair-Id=K3EI6M078Z3AC3"],  
+    ["https://datasets-server.huggingface.co/assets/Iris314/Food_tomatoes_dataset/--/bcca6b7b9e4be8378efc59e98a95f1c44a5055ae/--/default/original/43/image/image.jpg?Expires=1758728666&Signature=gMODl-rcaEShOGiZGyASzkT6idSpnsw0J5kJC5wKVthRIYP~RuBogRgeJ3XXWzWsflcaq75Guo1vQ20M5TzIdtPoVbMBcN65pTZJhfKZN9VLkA04ujNMCqOabKUWpVvR~UqqUzz8NbCpBE0Mwiu36vb8bFjVVpzaxE6BW-m0iEAHasUgttnXn6jjB1-OlK6z0SnCsLToNYcl4X7OjaJ0q0NSU1I6LusjYlPqmOTIvRl8ba2YXzE22X6DGlhEr3WNxYUzjT8~6nbkKUO2kWHi~2~i8aK1QNh4prNMjgUzoHSZcuBm2gDCdalpuXQWIqeLiaHAmvuybQclG4zRLpQhHw__&Key-Pair-Id=K3EI6M078Z3AC3"]
+    ]
+
+# Gradio UI
+with gradio.Blocks() as demo:
+
+    # Provide an introduction
+    gradio.Markdown("# Tomato or Not?")
+    gradio.Markdown("""
+    This is a simple app that uses the model at apsora/autoML_images_data to classify whether an image has a tomato or not, 
+    utilizing data found at Iris314/Food_tomatoes_dataset. To use the interface, upload an image in the area shown below.
+    """)
+    
+    # Interface for the incoming image
+    image_in = gradio.Image(type="pil", label="Input image", sources=["upload", "webcam"])  
+
+    # Interface elements to show htte result and probabilities
+    proba_pretty = gradio.Label(num_top_classes=2, label="Class probabilities") 
+
+    # Whenever a new image is uploaded, update the result
+    image_in.change(fn=do_predict, inputs=[image_in], outputs=[proba_pretty]) 
+
+      # For clickable example images
+    gradio.Examples(
+        examples=EXAMPLES,
+        inputs=[image_in],
+        label="Representative examples",
+        examples_per_page=8,
+        cache_examples=False,
+    )
+
+if __name__ == "__main__": 
+    demo.launch()