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app.py

@@ -1,65 +1,52 @@
-import os
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoProcessor
 from PIL import Image
 import torch
 
-def prompt_user_acceptance():
-    # Check environment variable
-    if os.getenv('TRUST_REMOTE_CODE') != 'true':
-        print("You must accept to continue. Set the environment variable TRUST_REMOTE_CODE=true.")
-        exit(1)
-
-def main():
-    prompt_user_acceptance()
-
-    # 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()
+# Load model and processor with trust_remote_code=True
+model = AutoModelForCausalLM.from_pretrained("mynkchaudhry/Florence-2-FT-DocVQA", force_download=True, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("mynkchaudhry/Florence-2-FT-DocVQA", force_download=True, trust_remote_code=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()