feat: add app code

app.py

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+import gradio as gr
+import torch
+from transformers import pipeline
+
+# 1. Initialize the zero-shot image classification pipeline using CLIP
+print("Loading OpenAI CLIP model...")
+classifier = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
+
+def classify_image(image, labels_text):
+    # Safe checks for missing inputs
+    if image is None or not labels_text.strip():
+        return {"Please upload an image and provide labels.": 1.0}
+    
+    # Clean up the comma-separated labels from the textbox input
+    candidate_labels = [label.strip() for label in labels_text.split(",") if label.strip()]
+    
+    if not candidate_labels:
+        return {"Please enter at least one valid label.": 1.0}
+    
+    # 2. Run inference through CLIP
+    # The pipeline automatically coordinates text tokens and image tensors
+    predictions = classifier(image, candidate_labels=candidate_labels)
+    
+    # 3. Format the response dictionary so Gradio's gr.Label can display it
+    # Format looks like: {"label_name": score_float}
+    return {pred["label"]: float(pred["score"]) for pred in predictions}
+
+# 4. Define the User Interface
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=[
+        gr.Image(type="pil", label="1. Upload your Image"),
+        gr.Textbox(
+            label="2. Candidate Labels (Separate with commas)", 
+            placeholder="e.g., a sunny beach, a cozy rainy day, a cute animal, corporate office",
+            value="a playful dog, a quiet cat, an outdoor landscape, indoor architecture"
+        )
+    ],
+    outputs=gr.Label(num_top_classes=5, label="Matching Confidence"),
+    title="CLIP Zero-Shot Image Matcher",
+    description="Type any descriptive phrases or labels you can think of, separate them with commas, and see how well OpenAI's CLIP aligns them to your uploaded photo.",
+    flagging_mode="never"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+torch
+transformers