Delete app.py

app.py

@@ -1,192 +0,0 @@
-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"
-
-# Function to install dependencies dynamically
-def install_dependencies():
-    packages = [
-        "pytesseract",
-        "torch==2.1.0",
-        "torchvision==0.16.0",
-        "torchaudio==2.1.0",
-        "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 package in packages:
-        subprocess.check_call([sys.executable, "-m", "pip", "install", package])
-        print(f"Installed {package}")
-
-install_dependencies()
-
-# Install system dependencies
-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
-model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "Qwen/Qwen2-VL-2B-Instruct-AWQ",
-    torch_dtype="auto",
-    use_auth_token=HUGGINGFACE_API_KEY
-)
-
-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(conn_str) 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)