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()