Update app.py

app.py

@@ -1,33 +1,64 @@
 import gradio as gr
 from transformers import AutoProcessor, AutoModelForImageTextToText
 from PIL import Image
+import re
 
-# Load model & processor once at startup
+# Load SmolDocling model & processor once
 processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
 model = AutoModelForImageTextToText.from_pretrained("ds4sd/SmolDocling-256M-preview")
 
-def smoldocling_readimage(image, prompt_text):
+def extract_fcel_values_from_image(image, prompt_text):
+    """Run SmolDocling on an image and return numeric values inside <fcel> tags."""
+    # Prepare prompt for the model
     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)
+
+    # Generate output
+    outputs = model.generate(**inputs, max_new_tokens=2048)
     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()
+    clean_text = result.replace("<end_of_utterance>", "").strip()
+
+    # Extract only <fcel> values
+    values = re.findall(r"<fcel>([\d.]+)", clean_text)
+    values = [float(v) for v in values]  # convert to floats
+
+    return values, clean_text
+
+def compare_images(image1, image2, prompt_text):
+    # Extract fcel values from both images
+    values1, raw1 = extract_fcel_values_from_image(image1, prompt_text)
+    values2, raw2 = extract_fcel_values_from_image(image2, prompt_text)
+
+    # Calculate accuracy
+    if len(values1) == len(values2) and values1 == values2:
+        accuracy = 100.0
+    else:
+        matches = sum(1 for a, b in zip(values1, values2) if a == b)
+        total = max(len(values1), len(values2))
+        accuracy = (matches / total) * 100 if total > 0 else 0
+
+    return {
+        "Extracted Values 1": values1,
+        "Extracted Values 2": values2,
+        "Accuracy (%)": accuracy
+    }
 
 # Gradio UI
 demo = gr.Interface(
-    fn=smoldocling_readimage,
+    fn=compare_images,
     inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Textbox(lines=1, placeholder="Enter prompt (e.g. Convert to docling)", label="Prompt"),
+        gr.Image(type="pil", label="Upload First Table Image"),
+        gr.Image(type="pil", label="Upload Second Table Image"),
+        gr.Textbox(lines=1, placeholder="Enter prompt (e.g. Extract table as OTSL)", label="Prompt")
     ],
-    outputs="text",
-    title="SmolDocling Web App",
-    description="Upload a document image and convert it to structured docling format."
+    outputs="json",
+    title="Table Data Accuracy Checker (SmolDocling)",
+    description="Uploads two table images, extracts only <fcel> values from OTSL output, and compares them for accuracy."
 )
 
-demo.launch()
+demo.launch()