Create app.py

app.py

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+from transformers import BlipProcessor, BlipForConditionalGeneration
+from gradio import Interface
+from PIL import Image
+
+# Load the BLIP-2 model and processor
+processor = BlipProcessor.from_pretrained("Salesforce/blip2-flan-t5-xl")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl")
+
+def generate_response(image, prompt):
+    """Generate a response from the model based on the image and prompt."""
+    inputs = processor(image, prompt, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    return processor.decode(outputs[0], skip_special_tokens=True)
+
+# Create a Gradio interface
+def predict(image, prompt):
+    return generate_response(image, prompt)
+
+interface = Interface(
+    fn=predict,
+    inputs=["image", "text"],
+    outputs="text",
+    title="BLIP-2: Introspective Monologue Generator",
+    description="Upload an image and provide a prompt. The model will respond with introspective thoughts about the image."
+)
+
+if __name__ == "__main__":
+    interface.launch()