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app.py

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+import gradio as gr
+from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+import torch
+# Load model and processor
+model_id = "pyimagesearch/finetuned_paligemma_vqav2_small"
+model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224")
+# Define inference function
+def process_image(image, prompt):
+   # Process the image and prompt using the processor
+   inputs = processor(image.convert("RGB"), prompt, return_tensors="pt")
+  
+   try:
+       # Generate output from the model
+       output = model.generate(**inputs, max_new_tokens=20)
+      
+       # Decode and return the output
+       decoded_output = processor.decode(output[0], skip_special_tokens=True)
+      
+       # Return the answer (exclude the prompt part from output)
+       return decoded_output[len(prompt):]
+   except IndexError as e:
+       print(f"IndexError: {e}")
+       return "An error occurred during processing."
+# Define the Gradio interface
+inputs = [
+   gr.Image(type="pil"),
+   gr.Textbox(label="Prompt", placeholder="Enter your question")
+]
+outputs = gr.Textbox(label="Answer")
+# Create the Gradio app
+demo = gr.Interface(fn=process_image, inputs=inputs, outputs=outputs, title="Visual Question Answering with Fine-tuned PaliGemma Model", description="Upload an image and ask questions to get answers.")
+# Launch the app
+demo.launch()

requirements.txt

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+transformers
+torch
+peft
+bitsandbytes
+gradio