initial commit

app.py

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+import gradio as gr
+from transformers import pipeline
+
+content_model = pipeline("image-classification", model="facebook/convnext-base-224")
+nsfw_model = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
+
+def analyze_image(image):
+    content_preds = content_model(image)
+    top_content = max(content_preds, key=lambda x: x["score"])
+    
+    nsfw_preds = nsfw_model(image)
+    top_nsfw = max(nsfw_preds, key=lambda x: x["score"])
+    
+    label = top_content["label"].lower()
+    is_human = "human" in label or "person" in label or "people" in label
+    is_dog = "dog" in label or "retriever" in label or "puppy" in label
+
+    adult_content = top_nsfw["label"].lower() == "nsfw"
+    violence = "blood" in label or "wound" in label
+    sensitive = adult_content or violence
+    
+    content_type = "human" if is_human else "dog" if is_dog else "unknown"
+    description = f"Imagem contendo {label}. Conteúdo {'seguro' if not sensitive else 'sensível'}."
+
+    return {
+        "content": content_type,
+        "adult_content": adult_content,
+        "violence": violence,
+        "sensitive_content": sensitive,
+        "content_description": description
+    }
+
+demo = gr.Interface(
+    fn=analyze_image,
+    inputs=gr.Image(type="pil"),
+    outputs="json",
+    title="Dog / Human Safety Detector"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+torch
+gradio