Update app.py

app.py

@@ -1,68 +1,75 @@
-import gradio as gr
-import torch
-from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
-import pytesseract
-
-# Set the Tesseract executable path (for Windows users)
-import pytesseract
-pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
-
-
-# Load the model and processor
-processor = LayoutLMv3Processor.from_pretrained("quadranttechnologies/Table_OCR")
-model = LayoutLMv3ForTokenClassification.from_pretrained("quadranttechnologies/Table_OCR")
-model.eval()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-def process_image(image):
-    try:
-        # Preprocess the image using the processor
-        encoding = processor(image, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
-
-        # Move inputs to the same device as the model
-        encoding = {key: val.to(device) for key, val in encoding.items()}
-
-        # Perform inference
-        with torch.no_grad():
-            outputs = model(**encoding)
-            predictions = torch.argmax(outputs.logits, dim=-1)
-
-        # Extract input IDs, bounding boxes, and predicted labels
-        words = encoding["input_ids"]
-        bboxes = encoding["bbox"]
-        labels = predictions.squeeze().tolist()
-
-        # Format output as JSON
-        structured_output = []
-        for word_id, bbox, label in zip(words.squeeze().tolist(), bboxes.squeeze().tolist(), labels):
-            # Decode the word ID to text
-            word = processor.tokenizer.decode([word_id]).strip()
-            if word:  # Avoid adding empty words
-                structured_output.append({
-                    "word": word,
-                    "bounding_box": bbox,
-                    "label": model.config.id2label[label]  # Convert label ID to label name
-                })
-
-        return structured_output
-
-    except Exception as e:
-        return {"error": str(e)}  # Return error details if any issue occurs
-
-# Define the Gradio interface
-interface = gr.Interface(
-    fn=process_image,
-    inputs=gr.Image(type="pil"),  # Accepts image input
-    outputs="json",  # Outputs JSON structure
-    title="Table OCR",
-    description="Upload an image (e.g., receipt or document) to extract structured information in JSON format."
-)
-
-# Launch the app
-if __name__ == "__main__":
-    interface.launch(share=True)
-
-
-
-
+import gradio as gr
+import torch
+from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
+import pytesseract
+
+import pytesseract
+
+# Explicitly set the Tesseract path
+# For Hugging Face Spaces, set this to the default Linux path
+pytesseract.pytesseract.tesseract_cmd = "/usr/bin/tesseract"
+
+# For local development on Windows
+# Uncomment the line below if running locally on Windows
+# pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
+
+
+
+# Load the model and processor
+processor = LayoutLMv3Processor.from_pretrained("quadranttechnologies/Table_OCR")
+model = LayoutLMv3ForTokenClassification.from_pretrained("quadranttechnologies/Table_OCR")
+model.eval()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def process_image(image):
+    try:
+        # Preprocess the image using the processor
+        encoding = processor(image, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
+
+        # Move inputs to the same device as the model
+        encoding = {key: val.to(device) for key, val in encoding.items()}
+
+        # Perform inference
+        with torch.no_grad():
+            outputs = model(**encoding)
+            predictions = torch.argmax(outputs.logits, dim=-1)
+
+        # Extract input IDs, bounding boxes, and predicted labels
+        words = encoding["input_ids"]
+        bboxes = encoding["bbox"]
+        labels = predictions.squeeze().tolist()
+
+        # Format output as JSON
+        structured_output = []
+        for word_id, bbox, label in zip(words.squeeze().tolist(), bboxes.squeeze().tolist(), labels):
+            # Decode the word ID to text
+            word = processor.tokenizer.decode([word_id]).strip()
+            if word:  # Avoid adding empty words
+                structured_output.append({
+                    "word": word,
+                    "bounding_box": bbox,
+                    "label": model.config.id2label[label]  # Convert label ID to label name
+                })
+
+        return structured_output
+
+    except Exception as e:
+        return {"error": str(e)}  # Return error details if any issue occurs
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil"),  # Accepts image input
+    outputs="json",  # Outputs JSON structure
+    title="Table OCR",
+    description="Upload an image (e.g., receipt or document) to extract structured information in JSON format."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch(share=True)
+
+
+
+