Delete app.py

app.py

@@ -1,57 +0,0 @@
-from PIL import Image
-from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
-import gradio as gr
-import torch
-import pytesseract
-import os 
-import os
-
-
-# Set the Linux path for Tesseract
-pytesseract.pytesseract.tesseract_cmd = "/usr/bin/tesseract"
-print("Tesseract version:", os.popen("tesseract --version").read())
-
-
-
-# Load the fine-tuned model and processor from local files
-model_path = "./"  # Path to the directory containing the uploaded model files
-model = LayoutLMv3ForTokenClassification.from_pretrained(model_path)
-processor = LayoutLMv3Processor.from_pretrained(model_path, apply_ocr=True)
-
-
-# Define label mapping
-id2label = {0: "company", 1: "date", 2: "address", 3: "total", 4: "other"}
-
-# Define prediction function
-def predict_receipt(image):
-    try:
-        # Preprocess the image
-        encoding = processor(image, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
-        input_ids = encoding["input_ids"]
-        attention_mask = encoding["attention_mask"]
-        bbox = encoding["bbox"]
-        pixel_values = encoding["pixel_values"]
-
-        # Get model predictions
-        outputs = model(input_ids=input_ids, attention_mask=attention_mask, bbox=bbox, pixel_values=pixel_values)
-        predictions = outputs.logits.argmax(-1).squeeze().tolist()
-
-        # Map predictions to labels
-        labeled_output = {id2label[pred]: idx for idx, pred in enumerate(predictions) if pred != 4}
-
-        return labeled_output
-    except Exception as e:
-        return {"error": str(e)}
-
-# Create Gradio Interface
-interface = gr.Interface(
-    fn=predict_receipt,
-    inputs=gr.Image(type="pil"),
-    outputs="json",
-    title="Receipt Information Analyzer",
-    description="Upload a scanned receipt image to extract information like company name, date, address, and total."
-)
-
-# Launch the interface
-if __name__ == "__main__":
-    interface.launch()