Create app.py

app.py

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+import subprocess
+import sys
+import os
+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
+import pymssql
+
+# Hardcoded Hugging Face token and SQL server IP address
+
+SERVER_IP = "35.227.148.156"
+
+# Install dependencies in smaller chunks to avoid memory issues
+def install_dependencies():
+    dependency_groups = [
+        ["pip==23.3.1", "setuptools", "wheel"],
+        ["pytesseract"],
+        ["torch==2.1.0+cpu", "torchvision==0.16.0+cpu", "torchaudio==2.1.0+cpu"],
+        ["transformers==4.38.2", "auto-gptq==0.7.1", "autoawq==0.2.8"],
+        ["qwen_vl_utils==0.0.8", "gradio==4.27.0"],
+        ["pyodbc", "sqlalchemy", "azure-storage-blob", "pymssql", "pandas", "opencv-python"]
+    ]
+    
+    for group in dependency_groups:
+        for package in group:
+            subprocess.check_call([sys.executable, "-m", "pip", "install", package], stdout=sys.stdout, stderr=sys.stderr)
+            print(f"Installed {package}")
+
+install_dependencies()
+
+# Install system dependencies (executed separately to avoid timeout issues)
+def install_system_dependencies():
+    commands = [
+        "apt-get update",
+        "apt-get install -y unixodbc-dev tesseract-ocr",
+        "ACCEPT_EULA=Y apt-get install -y msodbcsql17"
+    ]
+    for command in commands:
+        subprocess.run(command, shell=True, check=True)
+        print(f"Executed: {command}")
+
+install_system_dependencies()
+
+# Initialize model and processor with CPU mode
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2-VL-2B-Instruct-AWQ",
+    torch_dtype="auto",
+    use_auth_token=HUGGINGFACE_API_KEY
+)
+
+# Force model to use CPU to avoid memory issues on Hugging Face Spaces
+model.to("cpu")
+
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-AWQ", use_auth_token=HUGGINGFACE_API_KEY)
+
+pytesseract.pytesseract_cmd = r'/usr/bin/tesseract'
+
+# Function to preprocess the 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
+
+# Function to extract text using OCR
+def ocr_extract_text(image_path):
+    preprocessed_image = preprocess_image(image_path)
+    return pytesseract.image_to_string(preprocessed_image)
+
+# Function to process image and extract details
+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)
+
+# Function to parse details from extracted 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
+
+# Store extracted data in Azure SQL Database
+def store_to_azure_sql(dataframe):
+    conn_str = (
+        f"Driver={{ODBC Driver 17 for SQL Server}};"
+        f"Server={SERVER_IP};"
+        "Database=Invoices;"
+        "UID=pio-admin;"
+        "PWD=Poctest123#;"
+    )
+    try:
+        with pymssql.connect(SERVER_IP, "pio-admin", "Poctest123#", "Invoices") as conn:
+            cursor = conn.cursor()
+            create_table_query = """
+            IF NOT EXISTS (SELECT * FROM sysobjects WHERE name='Invoices' AND xtype='U')
+            CREATE TABLE Invoices (
+                InvoiceNumber NVARCHAR(255),
+                Date NVARCHAR(255),
+                Place NVARCHAR(255),
+                Amount NVARCHAR(255),
+                Category NVARCHAR(255)
+            )
+            """
+            cursor.execute(create_table_query)
+
+            for _, row in dataframe.iterrows():
+                insert_query = """
+                INSERT INTO Invoices (InvoiceNumber, Date, Place, Amount, Category)
+                VALUES (%s, %s, %s, %s, %s)
+                """
+                cursor.execute(insert_query, (row['Invoice Number'], row['Date'], row['Place'], row['Amount'], row['Category']))
+            conn.commit()
+            print("Data successfully stored in Azure SQL Database.")
+    except Exception as e:
+        print(f"Error storing data to database: {e}")
+
+# Gradio interface for invoice processing
+def gradio_interface(image_files):
+    results = []
+    for image_file in image_files:
+        details = process_image(image_file)
+        results.append(details)
+
+    df = pd.DataFrame(results)
+    store_to_azure_sql(df)
+    return df
+
+# Launch Gradio interface
+grpc_interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Files(label="Upload Invoice Images"),
+    outputs=gr.Dataframe(interactive=True),
+    title="Invoice Extraction System",
+)
+
+if __name__ == "__main__":
+    grpc_interface.launch(share=True)