Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+from PIL import Image
+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")
+    
+    # Print the inputs to debug
+    print("Processor outputs:", inputs)
+    
+    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()