Create app.py

app.py

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+import pdfplumber
+import pandas as pd
+import gradio as gr
+
+# Define function to extract data
+def extract_data(pdf_file):
+    data = []
+    columns = ["SI No", "Material Description", "Unit", "Quantity", "Dely Qty", "Dely Date", "Unit Rate", "Value"]
+    
+    start_si, end_si = 10, 1150
+
+    with pdfplumber.open(pdf_file) as pdf:
+        for page in pdf.pages:
+            text = page.extract_text().splitlines()
+            for line in text:
+                parts = line.split()
+                try:
+                    si_no = int(parts[0])
+                    if start_si <= si_no <= end_si:
+                        material_desc = " ".join(parts[1:3])
+                        unit = parts[3]
+                        quantity = int(parts[4])
+                        dely_qty = int(parts[5])
+                        dely_date = parts[6]
+                        unit_rate = float(parts[7])
+                        value = float(parts[8])
+                        data.append([si_no, material_desc, unit, quantity, dely_qty, dely_date, unit_rate, value])
+                except (ValueError, IndexError):
+                    continue
+
+    df = pd.DataFrame(data, columns=columns)
+    excel_path = "/tmp/Extracted_Purchase_Order_Data.xlsx"
+    df.to_excel(excel_path, index=False)
+    return excel_path
+
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=extract_data,
+    inputs=gr.File(label="Upload PDF"),
+    outputs=gr.File(label="Download Excel"),
+    title="PDF Data Extractor"
+)
+
+# Launch the app
+iface.launch()