Update app.py

app.py

@@ -1,42 +1,42 @@
-# import torch
-# from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
-# import gradio as gr
-# from PIL import Image
-
-# # Load model and processor
-# model_name = "google/pix2struct-docvqa-large"
-# model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
-# processor = Pix2StructProcessor.from_pretrained(model_name)
-
-# def process_image(image_path):
-#     try:
-#         # Load the image
-#         image = Image.open(image_path).convert("RGB")
-
-#         # Prepare the input
-#         inputs = processor(images=image, text="What does this image say?", return_tensors="pt")
-
-#         # Generate prediction
-#         output = model.generate(**inputs)
-
-#         # Decode the output
-#         solution = processor.decode(output[0], skip_special_tokens=True)
-#         return solution
-
-#     except Exception as e:
-#         return f"Error processing image: {str(e)}"
-
-# def predict(image):
-#     """Handles image input for Gradio."""
-#     return process_image(image)
-
-# # Gradio app
-# iface = gr.Interface(
-#     fn=predict,
-#     inputs=gr.Image(type="filepath"),
-#     outputs="text",
-#     title="Image Text Solution"
-# )
-
-# if __name__ == "__main__":
-#     iface.launch()
+import torch
+from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
+import gradio as gr
+from PIL import Image
+
+# Load model and processor
+model_name = "google/pix2struct-docvqa-large"
+model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
+processor = Pix2StructProcessor.from_pretrained(model_name)
+
+def process_image(image_path):
+    try:
+        # Load the image
+        image = Image.open(image_path).convert("RGB")
+
+        # Prepare the input
+        inputs = processor(images=image, text="What does this image say?", return_tensors="pt")
+
+        # Generate prediction
+        output = model.generate(**inputs)
+
+        # Decode the output
+        solution = processor.decode(output[0], skip_special_tokens=True)
+        return solution
+
+    except Exception as e:
+        return f"Error processing image: {str(e)}"
+
+def predict(image):
+    """Handles image input for Gradio."""
+    return process_image(image)
+
+# Gradio app
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="filepath"),
+    outputs="text",
+    title="Image Text Solution"
+)
+
+if __name__ == "__main__":
+    iface.launch()