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app.py

@@ -1,52 +1,66 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoProcessor
-from PIL import Image
-import torch
-
-# Load model and processor
-model = AutoModelForCausalLM.from_pretrained("mynkchaudhry/Florence-2-FT-DocVQA", force_download=True)
-processor = AutoProcessor.from_pretrained("mynkchaudhry/Florence-2-FT-DocVQA", force_download=True)
-
-def generate_response(image, question):
-    try:
-        if image.mode != "RGB":
-            image = image.convert("RGB")
-
-        inputs = processor(text=question, images=image, return_tensors="pt")
-
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        model.to(device)
-        inputs = {key: value.to(device) for key, value in inputs.items()}
-
-        generated_ids = model.generate(
-            input_ids=inputs["input_ids"],
-            pixel_values=inputs["pixel_values"],
-            max_length=1024,
-            num_beams=3,
-            early_stopping=True
-        )
-
-        response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        return response
-    except Exception as e:
-        return f"Error processing image: {e}"
-
-# Example images for demonstration (update paths as needed)
-examples = [
-    ["demo.png", "what is the address in the page?"],
-    ["demo2.jpg", "what is the date in the page?"],
-    ["demo.png", "what is the name in the page?"]
-]
-
-# Gradio interface
-iface = gr.Interface(
-    fn=generate_response,
-    inputs=[gr.Image(type="pil"), gr.Textbox(label="Question")],
-    outputs=gr.Textbox(label="Response"),
-    examples=examples,
-    title="Image to Text Extractor",
-    description="Upload an image and provide a question. This tool will extract the relevant information from the image based on your question."
-)
-
-# Launch the interface
-iface.launch()
+import os
+
+def prompt_user_acceptance():
+    response = input("Do you accept the execution of remote code from the model repository? (y/N): ").strip().lower()
+    if response != 'y':
+        print("You must accept to continue.")
+        exit(1)
+
+def main():
+    prompt_user_acceptance()
+
+    import gradio as gr
+    from transformers import AutoModelForCausalLM, AutoProcessor
+    from PIL import Image
+    import torch
+
+    # Load model and processor
+    model = AutoModelForCausalLM.from_pretrained("mynkchaudhry/Florence-2-FT-DocVQA", force_download=True)
+    processor = AutoProcessor.from_pretrained("mynkchaudhry/Florence-2-FT-DocVQA", force_download=True)
+
+    def generate_response(image, question):
+        try:
+            if image.mode != "RGB":
+                image = image.convert("RGB")
+
+            inputs = processor(text=question, images=image, return_tensors="pt")
+
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            model.to(device)
+            inputs = {key: value.to(device) for key, value in inputs.items()}
+
+            generated_ids = model.generate(
+                input_ids=inputs["input_ids"],
+                pixel_values=inputs["pixel_values"],
+                max_length=1024,
+                num_beams=3,
+                early_stopping=True
+            )
+
+            response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+            return response
+        except Exception as e:
+            return f"Error processing image: {e}"
+
+    # Example images for demonstration (update paths as needed)
+    examples = [
+        ["demo.png", "what is the address in the page?"],
+        ["demo2.jpg", "what is the date in the page?"],
+        ["demo.png", "what is the name in the page?"]
+    ]
+
+    # Gradio interface
+    iface = gr.Interface(
+        fn=generate_response,
+        inputs=[gr.Image(type="pil"), gr.Textbox(label="Question")],
+        outputs=gr.Textbox(label="Response"),
+        examples=examples,
+        title="Image to Text Extractor",
+        description="Upload an image and provide a question. This tool will extract the relevant information from the image based on your question."
+    )
+
+    # Launch the interface
+    iface.launch()
+
+if __name__ == "__main__":
+    main()