Create app.py

app.py

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+import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+
+# Load the image captioning model and processor
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+def generate_caption(image: Image.Image) -> str:
+    # Prepare the image for the model
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Generate caption
+    output = model.generate(**inputs)
+    
+    # Decode the caption
+    caption = processor.decode(output[0], skip_special_tokens=True)
+    return caption
+
+def run():
+    demo = gr.Interface(
+        fn=generate_caption,
+        inputs=gr.Image(type="pil"),
+        outputs="text",
+    )
+
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+
+if __name__ == "__main__":
+    run()