Delete app.py

app.py

@@ -1,28 +0,0 @@
-import gradio as gr from transformers import pipeline import docx2txt import PyPDF2 import os
-
-Load the summarizer model
-
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-
-Extract text based on file type
-
-def extract_text(file): ext = file.name.split(".")[-1].lower() if ext == "txt": return file.read().decode("utf-8") elif ext == "pdf": reader = PyPDF2.PdfReader(file) return "\n".join(page.extract_text() for page in reader.pages if page.extract_text()) elif ext == "docx": return docx2txt.process(file) else: return "Unsupported file type. Please upload a .pdf, .docx, or .txt file."
-
-Chunk long text for full-document summarization
-
-def chunk_text(text, max_length=1000): paragraphs = text.split("\n") chunks = [] current_chunk = "" for para in paragraphs: if len(current_chunk) + len(para) < max_length: current_chunk += para + "\n" else: chunks.append(current_chunk.strip()) current_chunk = para + "\n" if current_chunk: chunks.append(current_chunk.strip()) return chunks
-
-Full summarization function
-
-def summarize_input(text, file): source_text = text.strip() if text.strip() else extract_text(file) if not source_text: return "Please enter text or upload a valid file."
-
-chunks = chunk_text(source_text)
-summaries = [summarizer(chunk, max_length=130, min_length=30, do_sample=False)[0]['summary_text'] for chunk in chunks]
-return "\n\n".join(summaries)
-
-Gradio interface
-
-iface = gr.Interface( fn=summarize_input, inputs=[ gr.Textbox(lines=8, label="Enter text (optional)"), gr.File(label="Upload file (.txt, .pdf, .docx)", file_types=[".pdf", ".docx", ".txt"]) ], outputs=gr.Textbox(label="Summary"), title="📄 Smart Document Summarizer", description="Paste text or upload a document to get a full summary using a Hugging Face transformer." )
-
-iface.launch()
-