Update app.py

app.py

@@ -4,6 +4,7 @@ import torch
 import pdfplumber
 from docx import Document
 import io
+import os
 
 # Load the summarization model once
 device = 0 if torch.cuda.is_available() else -1
@@ -11,32 +12,40 @@ print(f"Using device: {'GPU' if device == 0 else 'CPU'}")
 
 summarizer = pipeline(
     "summarization",
-    model="sshleifer/distilbart-cnn-12-6",  # Fast & good for CPU; change to "facebook/bart-large-cnn" if you get GPU
+    model="sshleifer/distilbart-cnn-12-6",  # Fast & good for CPU
     device=device
 )
 
-def extract_text(file):
-    if file is None:
+def extract_text(file_path):
+    if file_path is None:
         return ""
-    filename = file.name.lower()
-    content = file.read()
+    
+    # file_path is a string (temp path) or NamedString-like object; convert to str
+    file_path = str(file_path)  # Ensure it's a plain string
+    filename = os.path.basename(file_path).lower()
+    
     try:
         if filename.endswith('.pdf'):
-            with pdfplumber.open(io.BytesIO(content)) as pdf:
+            with pdfplumber.open(file_path) as pdf:
                 return "\n".join(page.extract_text() or "" for page in pdf.pages)
+        
         elif filename.endswith('.docx'):
-            doc = Document(io.BytesIO(content))
+            doc = Document(file_path)
             return "\n".join(para.text for para in doc.paragraphs if para.text.strip())
+        
         elif filename.endswith('.txt'):
-            return content.decode('utf-8', errors='replace')
+            with open(file_path, "r", encoding="utf-8", errors="replace") as f:
+                return f.read()
+        
         else:
             return "Unsupported file. Please use .pdf, .docx, or .txt"
+    
     except Exception as e:
         return f"Error reading file: {str(e)}"
 
-def summarize(input_text, file, detail_level):
-    if file is not None:
-        text = extract_text(file)
+def summarize(input_text, file_path, detail_level):
+    if file_path is not None:
+        text = extract_text(file_path)
     else:
         text = input_text.strip()
 
@@ -47,7 +56,7 @@ def summarize(input_text, file, detail_level):
     if words < 100:
         return text  # Too short → return as-is
 
-    # Convert slider (0.15 to 0.60) to target length ratio
+    # Convert slider to target ratio
     target_ratio = detail_level
     target_length = int(words * target_ratio)
     max_l = max(500, min(1400, target_length + 250))
@@ -92,9 +101,8 @@ interface = gr.Interface(
     outputs=gr.Textbox(label="Generated Summary"),
     title="Lecture Summarizer",
     description="Upload a lecture file (PDF/DOCX/TXT) or paste text. Adjust the slider for shorter or more detailed summaries.",
-    flagging_mode="never",  # This disables flagging correctly
-    # NO theme here anymore
+    flagging_mode="never",
 )
 
-# Launch with theme here (Gradio 6.0+ requirement)
-interface.launch(theme="soft")  # Or gr.themes.Soft() if you want to import gr.themes
+# Launch with theme
+interface.launch(theme="soft")