Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+import joblib
+from transformers import CLIPProcessor, CLIPModel
+from huggingface_hub import hf_hub_download
+
+# --- Load CLIP Model & Processor from Hugging Face Hub ---
+clip_model = CLIPModel.from_pretrained("Ut14/clip-phone-view", subfolder="clip_model")
+clip_processor = CLIPProcessor.from_pretrained("Ut14/clip-phone-view", subfolder="clip_processor")
+
+# --- Download SVM model from Hugging Face Hub ---
+svm_model_path = hf_hub_download(repo_id="Ut14/clip-phone-view", filename="svm_phone_view_model.joblib")
+svm_model = joblib.load(svm_model_path)
+
+# --- Label Mapping ---
+label_map = {0: "Front", 1: "Back", 2: "Side"}
+
+# --- Extract Features ---
+def extract_clip_embedding(image: Image.Image) -> np.ndarray:
+    image = image.convert("RGB")
+    inputs = clip_processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        features = clip_model.get_image_features(**inputs)
+    return features.squeeze().numpy()
+
+# --- Prediction Function for Gradio ---
+def predict_view(image: Image.Image):
+    embedding = extract_clip_embedding(image)
+    pred = svm_model.predict([embedding])[0]
+    return label_map[pred]
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=predict_view,
+    inputs=gr.Image(type="pil", label="Upload Phone Image"),
+    outputs=gr.Label(num_top_classes=1, label="Predicted View"),
+    title="📱 Phone View Classifier",
+    description="Upload an image of a phone (front, back, or side) and get the predicted view using CLIP + SVM."
+)
+
+if __name__ == "__main__":
+    iface.launch()