Create app.py

Image caption app

app.py

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+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+import gradio as gr
+
+# Load BLIP processor and model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+def generate_caption(image):
+    try:
+        # No need to open the image, Gradio provides it as a PIL object
+        inputs = processor(images=image, return_tensors="pt")  # Use the image directly
+        
+        # Generate caption
+        outputs = model.generate(**inputs)
+        
+        # Decode and return the generated caption
+        caption = processor.decode(outputs[0], skip_special_tokens=True)
+        return caption
+    except Exception as e:
+        return f"Error generating caption: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Generated Caption"),  # Use Textbox for text output
+    title="Image Captioning with BLIP",
+    description="Generate captions for your images using the BLIP model."
+)
+
+# Launch the interface
+iface.launch()
+