Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+processor = AutoProcessor.from_pretrained(daliavanilla/BLIP-Radiology-model)
+model = BlipForConditionalGeneration.from_pretrained(daliavanilla/BLIP-Radiology-model)
+
+# Define the prediction function
+def generate_caption(image):
+    # Process the image
+    image = Image.fromarray(image)
+    #inputs = tokenizer(image, return_tensors="pt")
+    inputs = processor(images=image, return_tensors="pt")#.to(device)
+    pixel_values = inputs.pixel_values
+
+    # Generate caption
+    generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
+    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    return generated_caption
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(),
+    outputs=gr.Textbox(),
+    live=True
+)
+
+# Launch the Gradio interface
+interface.launch()