Update app.py

app.py

@@ -1,69 +1,69 @@
 
-import re
-import gradio as gr
-from transformers import AutoProcessor, AutoModelForImageTextToText
-from PIL import Image
+# import re
+# import gradio as gr
+# from transformers import AutoProcessor, AutoModelForImageTextToText
+# from PIL import Image
 
-# Load model & processor once at startup
-processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
-model = AutoModelForImageTextToText.from_pretrained("ds4sd/SmolDocling-256M-preview")
+# # Load model & processor once at startup
+# processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
+# model = AutoModelForImageTextToText.from_pretrained("ds4sd/SmolDocling-256M-preview")
 
-def smoldocling_readimage(image, prompt_text="Convert to docling"):
-    messages = [
-        {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt_text}]}
-    ]
-    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
-    inputs = processor(text=prompt, images=[image], return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=1024)
-    prompt_length = inputs.input_ids.shape[1]
-    generated = outputs[:, prompt_length:]
-    result = processor.batch_decode(generated, skip_special_tokens=False)[0]
-    return result.replace("<end_of_utterance>", "").strip()
+# def smoldocling_readimage(image, prompt_text="Convert to docling"):
+#     messages = [
+#         {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt_text}]}
+#     ]
+#     prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+#     inputs = processor(text=prompt, images=[image], return_tensors="pt")
+#     outputs = model.generate(**inputs, max_new_tokens=1024)
+#     prompt_length = inputs.input_ids.shape[1]
+#     generated = outputs[:, prompt_length:]
+#     result = processor.batch_decode(generated, skip_special_tokens=False)[0]
+#     return result.replace("<end_of_utterance>", "").strip()
 
-def extract_numbers(docling_text):
-    # Extract all floating numbers from the docling text using regex
-    numbers = re.findall(r"[-+]?\d*\.\d+|\d+", docling_text)
-    return list(map(float, numbers))
+# def extract_numbers(docling_text):
+#     # Extract all floating numbers from the docling text using regex
+#     numbers = re.findall(r"[-+]?\d*\.\d+|\d+", docling_text)
+#     return list(map(float, numbers))
 
-def compare_outputs(img1, img2):
-    # Extract docling text from both images
-    output1 = smoldocling_readimage(img1)
-    output2 = smoldocling_readimage(img2)
+# def compare_outputs(img1, img2):
+#     # Extract docling text from both images
+#     output1 = smoldocling_readimage(img1)
+#     output2 = smoldocling_readimage(img2)
     
-    # Extract numbers from both outputs
-    nums1 = extract_numbers(output1)
-    nums2 = extract_numbers(output2)
+#     # Extract numbers from both outputs
+#     nums1 = extract_numbers(output1)
+#     nums2 = extract_numbers(output2)
     
-    # Compare numbers — find matching count based on position
-    length = min(len(nums1), len(nums2))
-    matches = sum(1 for i in range(length) if abs(nums1[i] - nums2[i]) < 1e-3)
+#     # Compare numbers — find matching count based on position
+#     length = min(len(nums1), len(nums2))
+#     matches = sum(1 for i in range(length) if abs(nums1[i] - nums2[i]) < 1e-3)
     
-    # Calculate similarity accuracy percentage
-    total = max(len(nums1), len(nums2))
-    accuracy = (matches / total) * 100 if total > 0 else 0
+#     # Calculate similarity accuracy percentage
+#     total = max(len(nums1), len(nums2))
+#     accuracy = (matches / total) * 100 if total > 0 else 0
     
-    # Prepare result text
-    result_text = (
-        f"Output for Image 1:\n{output1}\n\n"
-        f"Output for Image 2:\n{output2}\n\n"
-        f"Similarity Accuracy: {accuracy:.2f}%\n"
-        f"Matching Values: {matches} out of {total}"
-    )
-    return result_text
+#     # Prepare result text
+#     result_text = (
+#         f"Output for Image 1:\n{output1}\n\n"
+#         f"Output for Image 2:\n{output2}\n\n"
+#         f"Similarity Accuracy: {accuracy:.2f}%\n"
+#         f"Matching Values: {matches} out of {total}"
+#     )
+#     return result_text
 
-# Gradio UI: take 2 images, output similarity report
-demo = gr.Interface(
-    fn=compare_outputs,
-    inputs=[
-        gr.Image(type="pil", label="Upload Image 1"),
-        gr.Image(type="pil", label="Upload Image 2"),
-    ],
-    outputs="text",
-    title="SmolDocling Image Comparison",
-    description="Upload two document images. This app extracts data from both and compares similarity."
-)
+# # Gradio UI: take 2 images, output similarity report
+# demo = gr.Interface(
+#     fn=compare_outputs,
+#     inputs=[
+#         gr.Image(type="pil", label="Upload Image 1"),
+#         gr.Image(type="pil", label="Upload Image 2"),
+#     ],
+#     outputs="text",
+#     title="SmolDocling Image Comparison",
+#     description="Upload two document images. This app extracts data from both and compares similarity."
+# )
 
-demo.launch()
+# demo.launch()
 
 
 import re