Delete test_azure_table.py

test_azure_table.py

@@ -1,92 +0,0 @@
-import requests
-import time
-import json
-import pandas as pd
-
-# Replace with your values
-endpoint = "https://tableextractwissen.cognitiveservices.azure.com/"
-api_key = "8rSFH0fdGvVxNfULZFjY3yqw9Zqy3gzVA6iHyoaAG6JzvwTvbBZfJQQJ99BDACYeBjFXJ3w3AAALACOGaGjX"
-
-# API endpoint for Layout Model v4.0
-analyze_url = f"{endpoint}/formrecognizer/documentModels/prebuilt-layout:analyze?api-version=2023-07-31"
-
-# Path to the PNG file
-file_path = "/mnt/SharedDisk/kushal/Infinity_Extractor/test_img_01.png"
-
-# Read image
-with open(file_path, "rb") as f:
-    file_bytes = f.read()
-
-# Request headers
-headers = {
-    "Content-Type": "image/png",
-    "Ocp-Apim-Subscription-Key": api_key
-}
-
-# Submit document
-response = requests.post(analyze_url, headers=headers, data=file_bytes)
-if response.status_code != 202:
-    raise Exception(f"Error: {response.text}")
-
-# Get polling URL
-result_url = response.headers["Operation-Location"]
-polling_headers = {
-    "Ocp-Apim-Subscription-Key": api_key
-}
-
-# Poll for result
-while True:
-    result_response = requests.get(result_url, headers=polling_headers)
-    result = result_response.json()
-    status = result["status"]
-    if status == "succeeded":
-        break
-    elif status == "failed":
-        raise Exception("Analysis failed:\n", json.dumps(result, indent=2))
-    time.sleep(2)
-
-# Extract tables
-tables = result.get("analyzeResult", {}).get("tables", [])
-final_dfs = []
-
-for idx, table in enumerate(tables):
-    row_count = table["rowCount"]
-    col_count = table["columnCount"]
-    
-    # Initialize blank grid
-    grid = [["" for _ in range(col_count)] for _ in range(row_count)]
-
-    # Sort cells by row and column index to avoid overwrite
-    sorted_cells = sorted(table["cells"], key=lambda x: (x["rowIndex"], x["columnIndex"]))
-
-    for cell in sorted_cells:
-        r = cell["rowIndex"]
-        c = cell["columnIndex"]
-        content = cell["content"]
-        row_span = cell.get("rowSpan", 1)
-        col_span = cell.get("columnSpan", 1)
-
-        for dr in range(row_span):
-            for dc in range(col_span):
-                rr = r + dr
-                cc = c + dc
-                if rr < row_count and cc < col_count:
-                    grid[rr][cc] = content
-
-    # Convert to DataFrame
-    df = pd.DataFrame(grid)
-    final_dfs.append(df)
-
-# Combine tables
-if len(final_dfs) == 1:
-    final_df = final_dfs[0]
-else:
-    final_df = pd.concat(final_dfs, keys=[f'Table_{i+1}' for i in range(len(final_dfs))])
-
-# Output
-print("\nFinal Extracted Table(s) with Spanning Support:\n")
-print(final_df)
-
-# Save to CSV
-# final_df.to_csv("extracted_table_with_spans.csv", index=False)
-#