Update app.py

app.py

@@ -0,0 +1,35 @@
+import pandas as pd
+from transformers import pipeline
+import os
+
+def generate_procurement_data():
+    # Simulating data generation from 10 to 1150 with incremental material numbers
+    data = {
+        "Sl No": list(range(10, 1160, 10)),
+        "Material Description": [f"BPS 017507, Material Number: 22073{str(i).zfill(5)}" for i in range(65400, 65400 + 1150, 10)],
+        "Unit": ["NO"] * 115,
+        "Quantity": [20] * 115,
+        "Dely Qty": [20] * 115,
+        "Dely Date": ["04.11.2024"] * 115,
+        "Unit Rate": [205.76] * 115,
+        "Value": [4115.2] * 115
+    }
+    
+    # Creating DataFrame
+    df = pd.DataFrame(data)
+    
+    # Saving to Excel
+    output_file = "Procurement_Summary_Full_Range.xlsx"
+    df.to_excel(output_file, index=False)
+    print(f"Excel file '{output_file}' generated successfully with procurement data.")
+    return output_file
+
+def main():
+    # Assuming an NLP model for possible text processing or future enhancement (optional)
+    # You can use a Hugging Face model pipeline, like a text summarization model, if needed for extraction.
+    print("Starting procurement data generation...")
+    output_file = generate_procurement_data()
+    print(f"Output file generated at: {output_file}")
+
+if __name__ == "__main__":
+    main()