Update app.py

app.py

@@ -1,40 +1,27 @@
-import gradio as gr
-from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
-from PIL import Image
-import pytesseract
-
-# Load model and processor
-model = LayoutLMv3ForTokenClassification.from_pretrained("./model")
-processor = LayoutLMv3Processor.from_pretrained("./model")
-
-# Define label mapping
-id2label = {0: "company", 1: "date", 2: "address", 3: "total", 4: "other"}
-
-def predict_receipt(image):
-    ocr_data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT)
-    texts = ocr_data["text"]
-    boxes = [
-        [
-            ocr_data["left"][i],
-            ocr_data["top"][i],
-            ocr_data["left"][i] + ocr_data["width"][i],
-            ocr_data["top"][i] + ocr_data["height"][i],
-        ]
-        for i in range(len(ocr_data["text"]))
-    ]
-    encoding = processor(image, text=texts, boxes=boxes, return_tensors="pt", truncation=True, padding="max_length")
-    outputs = model(**{k: v for k, v in encoding.items()})
-    predictions = outputs.logits.argmax(-1).squeeze().tolist()
-    labeled_output = {id2label[pred]: texts[i] for i, pred in enumerate(predictions) if pred != 4}
-    return labeled_output
-
-interface = gr.Interface(
-    fn=predict_receipt,
-    inputs=gr.Image(type="pil"),
-    outputs="json",
-    title="Receipt Analyzer",
-    description="Upload a receipt image to extract key information."
-)
-
-if __name__ == "__main__":
-    interface.launch()
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load your fine-tuned model and tokenizer
+model_name = "quadranttechnologies/Receipt_Image_Analyzer"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define a prediction function
+def analyze_receipt(receipt_text):
+    inputs = tokenizer(receipt_text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax(-1).item()
+    return f"Predicted Class: {predicted_class}"
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=analyze_receipt,
+    inputs="text",
+    outputs="text",
+    title="Receipt Image Analyzer",
+    description="Analyze receipts for relevant information using a fine-tuned LLM model.",
+)
+
+# Launch the Gradio app
+interface.launch()