Create app.py

app.py

@@ -0,0 +1,142 @@
+from flask import Flask, request, jsonify
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+import pandas as pd
+import pytesseract
+import cv2
+import pymssql
+
+app = Flask(__name__)
+
+# Initialize model and processor
+model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-AWQ", torch_dtype="auto")
+if torch.cuda.is_available():
+    model.to("cuda")
+
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-AWQ")
+pytesseract.pytesseract_cmd = r'/usr/bin/tesseract'
+
+# Function to identify category based on keywords
+def identify_category(text):
+    text = text.lower()
+    if any(keyword in text for keyword in ["food", "meal", "restaurant", "cafe", "coffee", "drink"]):
+        return "Food"
+    elif any(keyword in text for keyword in ["travel", "flight", "bus", "car", "taxi", "train", "ticket"]):
+        return "Travel"
+    elif any(keyword in text for keyword in ["hotel", "stay", "room", "resort", "accommodation"]):
+        return "Stay"
+    else:
+        return "Others"
+
+# Store DataFrame to Azure SQL Database
+def store_to_azure_sql(dataframe):
+    try:
+        conn = pymssql.connect(
+            server="piosqlserverbd.database.windows.net",
+            user="pio-admin",
+            password="Poctest123#",
+            database="PIOSqlDB"
+        )
+        cursor = conn.cursor()
+
+        create_table_query = """
+        IF NOT EXISTS (SELECT * FROM sysobjects WHERE name='Invoices' AND xtype='U')
+        CREATE TABLE Invoices (
+            EmployeeID NVARCHAR(50) NOT NULL PRIMARY KEY,
+            InvoiceNumber NVARCHAR(255),
+            Date NVARCHAR(255),
+            Place NVARCHAR(255),
+            Amount NVARCHAR(255),
+            Category NVARCHAR(255),
+            ApprovalStatus NVARCHAR(50) DEFAULT 'Pending'
+        )
+        """
+        cursor.execute(create_table_query)
+
+        cursor.execute("SELECT TOP 1 EmployeeID FROM Invoices ORDER BY EmployeeID DESC")
+        last_id = cursor.fetchone()
+        next_id = 0 if last_id is None else int(last_id[0]) + 1
+
+        for _, row in dataframe.iterrows():
+            category = identify_category(row["Invoice Details"])
+            insert_query = """
+            INSERT INTO Invoices (EmployeeID, InvoiceNumber, Date, Place, Amount, Category, ApprovalStatus)
+            VALUES (%s, %s, %s, %s, %s, %s, %s)
+            """
+            cursor.execute(
+                insert_query,
+                (
+                    f"{next_id:03d}",
+                    row.get("Invoice Number", "")[:255],
+                    row.get("Date", ""),
+                    row.get("Place", ""),
+                    row.get("Amount", ""),
+                    category,
+                    "Pending"
+                )
+            )
+            next_id += 1
+
+        conn.commit()
+        conn.close()
+        return "Data successfully stored in Azure SQL Database."
+    except Exception as e:
+        return f"Error storing data to database: {e}"
+
+# Process image and extract details
+def process_image(image_path):
+    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)
+    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+    output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+    return parse_details(output_text[0])
+
+def parse_details(details):
+    parsed_data = {
+        "Invoice Number": None,
+        "Date": None,
+        "Place": None,
+        "Amount": None,
+        "Invoice Details": details
+    }
+    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()
+    return parsed_data
+
+@app.route('/extract', methods=['POST'])
+def extract_invoice():
+    image_path = request.json.get('image_path')
+    extracted_data = process_image(image_path)
+    df = pd.DataFrame([extracted_data])
+    status = store_to_azure_sql(df)
+    return jsonify({"data": extracted_data, "status": status})
+
+if __name__ == '__main__':
+    app.run(port=22)