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README.md

@@ -1,12 +1,27 @@
 ---
-title: Test 1
-emoji: 🦀
-colorFrom: indigo
+title: Action Detection In Images
+emoji: 😻
+colorFrom: blue
 colorTo: pink
 sdk: gradio
 sdk_version: 4.31.5
 app_file: app.py
 pinned: false
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# General Action Classifier
+
+This is a Gradio interface that allows users to upload an image and specify candidate labels to check if a certain action is present in the image. The app uses a CLIP-ViT model to classify the image based on the provided labels.
+
+## How to Use
+
+1. Upload an image.
+2. Enter candidate labels separated by commas.
+3. The app will classify the image and display the results.
+
+## Example
+
+For instance, to check if a person is holding a beverage, you can enter labels like: "human with beverage, human, beverage".

app.py

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+import gradio as gr
+from PIL import ImageFilter, Image
+from transformers import AutoModelForZeroShotImageClassification, AutoProcessor
+import torch
+
+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(",") if label.strip()] + ["other"]
+    
+    if len(candidate_labels) == 1:
+        candidate_labels.append("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": float(score), "label": candidate_label}
+        for score, candidate_label in sorted(zip(probs, candidate_labels), key=lambda x: -x[0])
+    ]
+
+    # Convert results to list of lists for Dataframe
+    results_for_df = [[res['label'], res['score']] for res in results]
+
+    # Decision-making logic
+    top_label = results[0]["label"]
+    second_label = results[1]["label"] if len(results) > 1 else "None"
+
+    # 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}" if len(results) > 1 else "")
+
+    # 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_for_df, messages
+
+# Default values
+default_labels = "human with beverage,human,beverage"
+default_image_path = "F50xXeBbcAA0IIx.jpeg"
+
+# Load default image
+default_image = Image.open(default_image_path)
+
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload an Image", value=default_image),
+        gr.Textbox(label="Candidate Labels (comma separated)", value=default_labels)
+    ],
+    outputs=[
+        gr.Label(label="Result"),
+        gr.Textbox(label="Top Label"),
+        gr.Dataframe(headers=["Label", "Score"], label="Details"),
+        gr.Textbox(label="Messages")
+    ],
+    title="General Action Classifier",
+    description="""
+    **Instructions:**
+    
+    1. **Upload an Image**: Drag and drop an image or click to upload an image file. A default image is provided.
+    
+    2. **Enter Candidate Labels**: 
+       - Provide candidate labels separated by commas. 
+       - For example: `human with beverage,human,beverage`
+       - The label "other" will automatically be added to the list of candidate labels.
+       - You can enter just one label, and "other" will still be added automatically. Default labels are provided.
+    
+    3. **View Results**: 
+       - The result will indicate whether the specified action (top label) is present in the image.
+       - Detailed scores for each label will be displayed in a table.
+       - Additional messages explaining the decision process will also be shown.
+    """
+)
+
+if __name__ == "__main__":
+    iface.launch()

gitattributes

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requirements.txt

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+gradio
+transformers
+torch
+pillow
+requests