Create app.py

app.py

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+import gradio as gr
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+import pandas as pd
+import pytesseract
+import cv2
+
+# Set Tesseract command (only works if Tesseract is already installed on the hosting server)
+pytesseract.pytesseract_cmd = r'/usr/bin/tesseract'
+
+# Initialize the model and processor
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2-VL-2B-Instruct-AWQ",
+    torch_dtype="auto"
+)
+model.to("cpu")
+
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-AWQ")
+
+
+# Preprocessing image for OCR
+def preprocess_image(image_path):
+    image = cv2.imread(image_path)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    _, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
+    return binary
+
+
+# OCR-based text extraction
+def ocr_extract_text(image_path):
+    preprocessed_image = preprocess_image(image_path)
+    return pytesseract.image_to_string(preprocessed_image)
+
+
+# Model-based image processing
+def process_image(image_path):
+    try:
+        messages = [{
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image_path},
+                {"type": "text", "text": (
+                    "Extract the following details from the invoice:\n"
+                    "- 'invoice_number'\n"
+                    "- 'date'\n"
+                    "- 'place'\n"
+                    "- 'amount' (monetary value in the relevant currency)\n"
+                    "- 'category' (based on the invoice type)"
+                )}
+            ]
+        }]
+
+        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        image_inputs, video_inputs = process_vision_info(messages)
+        inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
+        inputs = inputs.to(model.device)
+
+        generated_ids = model.generate(**inputs, max_new_tokens=128)
+        output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+
+        return parse_details(output_text[0])
+
+    except Exception as e:
+        print(f"Model failed, falling back to OCR: {e}")
+        ocr_text = ocr_extract_text(image_path)
+        return parse_details(ocr_text)
+
+
+# Parsing details from text
+def parse_details(details):
+    parsed_data = {
+        "Invoice Number": None,
+        "Date": None,
+        "Place": None,
+        "Amount": None,
+        "Category": None
+    }
+
+    lines = details.split("\n")
+    for line in lines:
+        lower_line = line.lower()
+        if "invoice" in lower_line:
+            parsed_data["Invoice Number"] = line.split(":")[-1].strip()
+        elif "date" in lower_line:
+            parsed_data["Date"] = line.split(":")[-1].strip()
+        elif "place" in lower_line:
+            parsed_data["Place"] = line.split(":")[-1].strip()
+        elif any(keyword in lower_line for keyword in ["total", "amount", "cost"]):
+            parsed_data["Amount"] = line.split(":")[-1].strip()
+        else:
+            parsed_data["Category"] = "General"
+
+    return parsed_data
+
+
+# Gradio Interface
+def gradio_interface(image_files):
+    results = []
+    for image_file in image_files:
+        details = process_image(image_file.name)
+        results.append(details)
+
+    df = pd.DataFrame(results)
+    return df
+
+
+# Launch Gradio App
+grpc_interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.File(label="Upload Invoice Images", file_types=["image"]),
+    outputs=gr.Dataframe(interactive=True),
+    title="Invoice Extraction System"
+)
+
+if __name__ == "__main__":
+    grpc_interface.launch(share=True)