Update app.py

app.py

@@ -4,12 +4,12 @@ import pandas as pd
 import re  
 import io  
 
-def extract_with_lines(pdf_file):  
+def extract_with_lines(pdf_path):  
     """  
     Extract all PDF text, displaying page+line number prefix.  
     Returns raw text for training.  
     """  
-    with open(pdf_file.name, "rb") as f:  
+    with open(pdf_path, "rb") as f:  
         reader = PyPDF2.PdfReader(f)  
         result = []  
         for i, page in enumerate(reader.pages):  
@@ -40,89 +40,66 @@ def guess_extraction_regex(sample_value, all_lines):
     Use the sample_value to build a simple extraction pattern.  
     If the value is after a colon or consistent header, match similar lines.  
     """  
-    # Try to extract prefix  
     for line in all_lines:  
         if sample_value in line:  
-            # If the sample is after "Some Label: ", extract that  
             if ':' in line:  
                 prefix, suffix = line.split(':', 1)  
                 if sample_value.strip() == suffix.strip():  
-                    return re.compile(f"{re.escape(prefix.strip())}\s*:\s*(.+)", re.IGNORECASE)  
-            # If the sample is always after the same start  
-            match = re.match(r"(.*?)(\s+)?"+re.escape(sample_value)+r"(.*)?", line)  
+                    return re.compile(f"{re.escape(prefix.strip())}\\s*:\\s*(.+)", re.IGNORECASE)  
+            match = re.match(r"(.*?)(\\s+)?"+re.escape(sample_value)+r"(.*)?", line)  
             if match and match.group(1).strip():  
-                # Return a regex that matches that prefix and captures the rest  
-                return re.compile(f"{re.escape(match.group(1).strip())}\s*(.+)", re.IGNORECASE)  
-    # Fallback: find lines that contain the sample and grab same structure  
+                return re.compile(f"{re.escape(match.group(1).strip())}\\s*(.+)", re.IGNORECASE)  
     return None  
 
 def extract_table_from_sample(raw_text, label, sample_value):  
-    # Split lines  
     lines = raw_text.splitlines()  
     if not label or not sample_value:  
         return pd.DataFrame([{"Error": "Please supply both label and sample value!"}])  
-
-    # Try to pattern match (e.g. "Customer Name: Ramesh Kumar")  
     regex = guess_extraction_regex(sample_value, lines)  
     found = []  
-
     if regex:  
         for line in lines:  
             m = regex.match(line)  
             if m:  
                 found.append({label: m.group(1).strip()})  
     else:  
-        # Fallback, just grab lines that contain the sample's prefix  
-        # Try to find all lines which have the non-digit prefix of this sample  
         prefix = sample_value[:5]  
         for line in lines:  
             if prefix in line:  
                 found.append({label: line.strip()})  
-
     if not found:  
         return pd.DataFrame([{"Error": f"No matches found for sample: {sample_value}"}])  
     return pd.DataFrame(found)  
 
 def export_xlsx(df):  
-    """Export pandas df to xlsx in-memory file"""  
     buf = io.BytesIO()  
     with pd.ExcelWriter(buf, engine="xlsxwriter") as writer:  
         df.to_excel(writer, index=False)  
     buf.seek(0)  
     return buf  
 
-### Gradio Interface  
-
 with gr.Blocks() as demo:  
     gr.Markdown("# 🧑‍🏫 PDF Teach-&-Extract System\n**1. Upload PDF → 2. Teach a sample field → 3. Preview all auto-extracted matches → 4. Download as Excel**")  
-
-    file_in = gr.File(label="Upload your PDF", file_count="single", type="file")  
+    file_in = gr.File(label="Upload your PDF", file_count="single", type="filepath")  
     raw_text = gr.Textbox(label="Raw extracted PDF text (preview/copy here)", lines=18, show_copy_button=True)  
-
     file_in.change(extract_with_lines, inputs=file_in, outputs=raw_text)  
-
     with gr.Row():  
         teach_label = gr.Textbox(label="Your Desired Field Name (e.g. Customer Name)")  
         teach_sample = gr.Textbox(label="Example Value (copy-paste from above)")  
         teach_search = gr.Button("Show Context")  
     context_out = gr.Textbox(label="System shows the found context(s)", lines=4)  
-
     teach_search.click(get_sample_context, inputs=[raw_text, teach_sample], outputs=context_out)  
-
     with gr.Row():  
         extract_btn = gr.Button("Extract All Similar Values")  
         results_table = gr.Dataframe(label="Extracted Results Table")  
         download_btn = gr.Button("Download as Excel")  
         xlsx_file = gr.File(label="Excel Download (.xlsx)", visible=True)  
-
     def extract_and_preview(raw_text, teach_label, teach_sample):  
         df = extract_table_from_sample(raw_text, teach_label, teach_sample)  
         return df  
     extract_btn.click(extract_and_preview, inputs=[raw_text, teach_label, teach_sample], outputs=results_table)  
-
     def save_xlsx(df):  
         buf = export_xlsx(df)  
         return ("results.xlsx", buf)  
     download_btn.click(save_xlsx, inputs=results_table, outputs=xlsx_file)  
-
 demo.launch()