Delete app.py

app.py

@@ -1,247 +0,0 @@
-import base64
-import json
-import requests
-import datetime
-import hashlib
-import hmac
-import logging
-import ntplib
-import time
-import os
-import tempfile
-import io
-from openai import OpenAI
-from openpyxl import Workbook
-import gradio as gr
-import re
-import fitz  # PyMuPDF
-import pandas as pd
-from gradio_pdf import PDF  # Import the new PDF component
-
-# Configure logging
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
-
-# Get configuration from environment variables
-SECRET_ID = os.getenv("SECRET_ID", "AKID9EGD5tdKtpq5V1pkfbkwcJLOLEFVnJwp")
-SECRET_KEY = os.getenv("SECRET_KEY", "374ugKueFkK7DFA62675Gk9TizCGA49A")
-REGION = os.getenv("REGION", "ap-guangzhou")
-ENDPOINT = os.getenv("ENDPOINT", "lke.tencentcloudapi.com")
-SERVICE = "lke"
-ACTION = "ReconstructDocument"
-VERSION = "2023-11-30"
-
-# OpenAI API key
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY",
-                           "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")
-
-
-# Get NTP time
-def get_ntp_time():
-    ntp_client = ntplib.NTPClient()
-    try:
-        response = ntp_client.request('pool.ntp.org', version=3, timeout=5)
-        return datetime.datetime.fromtimestamp(response.tx_time, datetime.timezone.utc)
-    except Exception as e:
-        logging.warning(f"Unable to get NTP time, using local time: {e}")
-        return datetime.datetime.now(datetime.timezone.utc)
-
-
-# Signing function
-def sign(key, msg):
-    return hmac.new(key, msg.encode("utf-8"), hashlib.sha256).digest()
-
-
-# Get authentication information
-def get_auth(secret_id, secret_key, host, method, params, headers):
-    algorithm = "TC3-HMAC-SHA256"
-    ntp_time = get_ntp_time()
-    timestamp = int(ntp_time.timestamp())
-    date = ntp_time.strftime('%Y-%m-%d')
-
-    http_request_method = method.upper()
-    canonical_uri = "/"
-    canonical_querystring = ""
-    ct = headers.get("content-type", "application/x-www-form-urlencoded")
-    payload = json.dumps(params)
-    canonical_headers = f"content-type:{ct}\nhost:{host}\n"
-    signed_headers = "content-type;host"
-    hashed_request_payload = hashlib.sha256(payload.encode("utf-8")).hexdigest()
-    canonical_request = (f"{http_request_method}\n{canonical_uri}\n{canonical_querystring}\n"
-                         f"{canonical_headers}\n{signed_headers}\n{hashed_request_payload}")
-
-    credential_scope = f"{date}/{SERVICE}/tc3_request"
-    hashed_canonical_request = hashlib.sha256(canonical_request.encode("utf-8")).hexdigest()
-    string_to_sign = (f"{algorithm}\n{timestamp}\n{credential_scope}\n{hashed_canonical_request}")
-
-    secret_date = sign(f"TC3{secret_key}".encode("utf-8"), date)
-    secret_service = sign(secret_date, SERVICE)
-    secret_signing = sign(secret_service, "tc3_request")
-    signature = hmac.new(secret_signing, string_to_sign.encode("utf-8"), hashlib.sha256).hexdigest()
-
-    authorization = (f"{algorithm} Credential={secret_id}/{credential_scope}, "
-                     f"SignedHeaders={signed_headers}, Signature={signature}")
-
-    return {
-        "Authorization": authorization,
-        "Host": host,
-        "Content-Type": ct,
-        "X-TC-Timestamp": str(timestamp),
-        "X-TC-Version": VERSION,
-        "X-TC-Action": ACTION,
-        "X-TC-Region": REGION
-    }
-
-
-# Extract information
-def extract_information(content):
-    client = OpenAI(api_key=OPENAI_API_KEY)
-
-    prompt = (
-        "There are some guides, respond in detailed content, respond without content in (), JSON begin with contracts value:\n"
-        "1. Contract awarded date\n"
-        "2. Construction location (This part of the content is in the title, not in the table; the address must be returned and should be detailed.)\n"
-        "3. Tender reference\n"
-        "4. Construction summary (in the 'particular' section)\n"
-        "5. Contractor\n"
-        "6. Contractor address(this is not company name, the address must be returned and should be detailed.)\n"
-        "7. Amount\n"
-        "8. Notice publish date (at the end of the content)"
-    )
-
-    for attempt in range(3):  # Try three times
-        try:
-            logging.info(f"Extracting information (Attempt {attempt + 1}/3)")
-            response = client.chat.completions.create(
-                model="gpt-4o",
-                messages=[
-                    {"role": "system", "content": "You are a helpful assistant designed to output JSON"},
-                    {"role": "user", "content": f"{prompt}\n\n{content}"}
-                ],
-                response_format={"type": "json_object"}
-            )
-
-            if response.choices[0].finish_reason == "stop":
-                extracted_info = json.loads(response.choices[0].message.content)
-                return json.dumps(extracted_info, ensure_ascii=False, indent=4)
-            else:
-                logging.warning(f"Warning: Unexpected completion reason - {response.choices[0].finish_reason}")
-        except Exception as e:
-            logging.error(f"Error: API call failed - {str(e)}")
-
-        if attempt < 2:  # If not the last attempt, wait before retrying
-            time.sleep(5)
-
-    return None  # If all three attempts fail, return None.
-
-
-# JSON to Excel
-def json_to_excel(json_data):
-    data = json.loads(json_data)
-
-    wb = Workbook()
-    ws = wb.active
-
-    headers = ['contract_awarded_date', 'construction_location', 'tender_reference',
-               'construction_summary', 'contractor', 'contractor_address',
-               'amount', 'notice_publish_date']
-    ws.append(headers)
-
-    # Create a helper function for exact matching
-    def exact_match(key, target):
-        key = ''.join(c.lower() for c in key if c.isalnum())
-        target = ''.join(c.lower() for c in target if c.isalnum())
-        return key == target
-
-    for contract in data['contracts']:
-        row = []
-        for header in headers:
-            # Use exact matching to find the corresponding value
-            matched_value = next((v for k, v in contract.items() if exact_match(header, k)), '')
-            row.append(matched_value)
-        ws.append(row)
-
-    with tempfile.NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
-        wb.save(tmp.name)
-        return tmp.name
-
-def clean_url(input_text):
-    # Remove any leading or trailing quotes
-    cleaned_url = input_text.strip().strip('"')
-    return cleaned_url
-
-# New function: Process uploaded PDF
-def process_pdf(file):
-    logging.info(f"Start processing PDF file: {type(file)}")
-    try:
-        if hasattr(file, 'name'):
-            # If file is a file object
-            with fitz.open(file.name) as doc:
-                text_content = ""
-                for page in doc:
-                    text_content += page.get_text()
-        else:
-            # If file is a string (file path)
-            with fitz.open(file) as doc:
-                text_content = ""
-                for page in doc:
-                    text_content += page.get_text()
-        logging.info("PDF processing successful")
-        return text_content
-    except Exception as e:
-        logging.error(f"PDF processing error: {str(e)}")
-        raise
-
-def preview_excel(excel_path):
-    try:
-        df = pd.read_excel(excel_path, nrows=10)
-        preview_df = df.iloc[:10, :8]
-        return gr.Dataframe(value=preview_df)
-    except Exception as e:
-        logging.error(f"Excel preview error: {str(e)}")
-        return gr.Dataframe()
-
-def process_pdf_file(file):
-    if file is None:
-        logging.warning("No file uploaded")
-        return "Please upload a PDF file.", None, gr.Dataframe()
-
-    try:
-        logging.info(f"Received file: {type(file)}, {file.name if hasattr(file, 'name') else 'No name'}")
-        pdf_content = process_pdf(file)
-    except Exception as e:
-        logging.error(f"Error processing PDF file: {str(e)}", exc_info=True)
-        return f"Error processing PDF file: {str(e)}", None, gr.Dataframe()
-
-    try:
-        json_data = extract_information(pdf_content)
-        if json_data is None:
-            logging.error("Failed to extract information")
-            return "Error extracting information. Please try again later.", None, gr.Dataframe()
-
-        excel_path = json_to_excel(json_data)
-        excel_preview = preview_excel(excel_path)
-
-        logging.info("File processing successful")
-        return "Processing successful!", excel_path, excel_preview
-    except Exception as e:
-        logging.error(f"Error processing file: {str(e)}", exc_info=True)
-        return f"Error processing file: {str(e)}", None, gr.Dataframe()
-
-# Gradio interface
-iface = gr.Interface(
-    fn=process_pdf_file,
-    inputs=[
-        PDF(label="Upload PDF File")  # Only keep the label parameter
-    ],
-    outputs=[
-        gr.Textbox(label="Processing Status"),
-        gr.File(label="Download Excel File"),
-        gr.Dataframe(label="Excel Preview (First 10 rows, 8 columns)")
-    ],
-    title="PDF Document Processing and Information Extraction",
-    description="Upload a PDF file, and the system will process it and generate an Excel result."
-)
-
-# Run the Gradio app
-if __name__ == "__main__":
-    iface.launch()