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app.py

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+# app.py
+import os
+import torch
+import numpy as np
+from PIL import Image
+import joblib
+import gradio as gr
+from transformers import CLIPProcessor, CLIPModel
+
+# --- Load CLIP Model and Processor ---
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# --- Load Trained SVM Model ---
+svm_model = joblib.load("svm_phone_view_model.joblib")
+
+# --- Label Mapping ---
+label_map = {0: "back", 1: "bottom", 2: "front", 3: "top"}
+
+# --- Function to Extract CLIP Embedding ---
+def extract_clip_embedding(image):
+    inputs = clip_processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        features = clip_model.get_image_features(**inputs)
+    return features.squeeze().numpy()
+
+# --- Gradio prediction function ---
+def predict_image_view(image):
+    embedding = extract_clip_embedding(image)
+    probs = svm_model.predict_proba([embedding])[0]
+    pred_index = np.argmax(probs)
+    prediction = label_map[pred_index]
+    confidence = probs[pred_index] * 100
+    return f"View: {prediction.upper()} ({confidence:.2f}%)"
+
+# --- Launch Gradio interface ---
+demo = gr.Interface(
+    fn=predict_image_view,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Phone View Classifier (4-class)",
+    description="Upload an image of a phone and classify it as one of: Front, Back, Top, Bottom"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch>=2.0.0
+transformers>=4.30.0
+scikit-learn>=1.3.0
+joblib>=1.3.2
+Pillow>=9.5.0
+numpy>=1.24.0
+tqdm>=4.65.0
+gradio