Create app.py

app.py

@@ -0,0 +1,80 @@
+import gradio as gr
+from PIL import ImageFilter, Image
+from transformers import AutoModelForZeroShotImageClassification, AutoProcessor
+import torch
+import requests
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Initialize the CLIP-ViT model
+checkpoint = "openai/clip-vit-large-patch14-336"
+model = AutoModelForZeroShotImageClassification.from_pretrained(checkpoint)
+model = model.to(device)
+
+processor = AutoProcessor.from_pretrained(checkpoint)
+
+def classify_image(image, candidate_labels):
+    messages = []
+    candidate_labels = [label.strip() for label in candidate_labels.split(",")] + ["other"]
+    
+    # Blur the image
+    image = image.filter(ImageFilter.GaussianBlur(radius=5))
+    
+    # Process the image and candidate labels
+    inputs = processor(images=image, text=candidate_labels, return_tensors="pt", padding=True)
+    inputs = {key: val.to(device) for key, val in inputs.items()}
+
+    # Get model's output
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits_per_image[0]
+    probs = logits.softmax(dim=-1).cpu().numpy()
+
+    # Organize results
+    results = [
+        {"score": score, "label": candidate_label}
+        for score, candidate_label in sorted(zip(probs, candidate_labels), key=lambda x: -x[0])
+    ]
+
+    # Decision-making logic
+    top_label = results[0]["label"]
+    second_label = results[1]["label"]
+
+    # Add messages to understand the scores
+    messages.append(f"Top label: {top_label} with score: {results[0]['score']:.2f}")
+    messages.append(f"Second label: {second_label} with score: {results[1]['score']:.2f}")
+
+    # Example decision logic for specific scenarios (can be customized further)
+    if top_label == candidate_labels[0] and results[0]["score"] >= 0.58 and second_label != "other":
+        messages.append("Triggered the new 0.58 check!")
+        result = True
+    elif top_label == candidate_labels[0] and second_label in candidate_labels[:-1] and (results[0]['score'] + results[1]['score']) >= 0.90:
+        messages.append("Triggered the 90% combined check!")
+        result = True
+    elif top_label == candidate_labels[1] and second_label == candidate_labels[0] and (results[0]['score'] + results[1]['score']) >= 0.95:
+        messages.append("Triggered the 90% reverse order check!")
+        result = True
+    else:
+        result = False
+
+    return result, top_label, results, messages
+
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload an Image"),
+        gr.Textbox(label="Candidate Labels (comma separated)")
+    ],
+    outputs=[
+        gr.Label(label="Result"),
+        gr.Textbox(label="Top Label"),
+        gr.Dataframe(label="Details"),
+        gr.Textbox(label="Messages")
+    ],
+    title="General Action Classifier",
+    description="Upload an image and specify candidate labels to check if an action is present in the image."
+)
+
+if __name__ == "__main__":
+    iface.launch()