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app.py

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+
+import gradio as gr
+from transformers import pipeline
+import docx2txt
+import PyPDF2
+from docx import Document
+from fpdf import FPDF
+import os
+from io import BytesIO
+
+# Load models
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+translator_hi_en = pipeline("translation", model="Helsinki-NLP/opus-mt-hi-en")
+translator_mr_en = pipeline("translation", model="Helsinki-NLP/opus-mt-mr-en")
+translator_en_hi = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
+translator_en_mr = pipeline("translation", model="Helsinki-NLP/opus-mt-en-mr")
+
+# 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 translation and 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
+
+# Translate to English from selected language
+def translate_to_english(text, lang):
+    if lang == "Hindi":
+        return " ".join([translator_hi_en(chunk)[0]['translation_text'] for chunk in chunk_text(text, 500)])
+    elif lang == "Marathi":
+        return " ".join([translator_mr_en(chunk)[0]['translation_text'] for chunk in chunk_text(text, 500)])
+    return text
+
+# Translate from English to selected output language
+def translate_from_english(text, lang):
+    if lang == "Hindi":
+        return " ".join([translator_en_hi(chunk)[0]['translation_text'] for chunk in chunk_text(text, 500)])
+    elif lang == "Marathi":
+        return " ".join([translator_en_mr(chunk)[0]['translation_text'] for chunk in chunk_text(text, 500)])
+    return text
+
+# Save summary to DOCX
+def generate_docx(text):
+    doc = Document()
+    doc.add_heading("Summary", 0)
+    doc.add_paragraph(text)
+    buffer = BytesIO()
+    doc.save(buffer)
+    buffer.seek(0)
+    return buffer
+
+# Save summary to PDF
+def generate_pdf(text):
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+    for line in text.split("\n"):
+        pdf.multi_cell(0, 10, line)
+    buffer = BytesIO()
+    pdf.output(buffer)
+    buffer.seek(0)
+    return buffer
+
+# Main summarization function
+def summarize_input(text, file, length, input_lang, output_lang):
+    source_text = text.strip() if text.strip() else extract_text(file)
+    if not source_text:
+        return "", None, None
+
+    # Translate to English if needed
+    if input_lang != "English":
+        source_text = translate_to_english(source_text, input_lang)
+
+    # Set summary length
+    if length == "Short (1–2 sentences)":
+        min_len, max_len = 20, 60
+    elif length == "Detailed (paragraph)":
+        min_len, max_len = 80, 200
+    else:
+        min_len, max_len = 40, 130
+
+    chunks = chunk_text(source_text)
+    summaries = [summarizer(chunk, max_length=max_len, min_length=min_len, do_sample=False)[0]['summary_text'] for chunk in chunks]
+    final_summary = "\n\n".join(summaries)
+
+    # Translate from English to output language
+    if output_lang != "English":
+        final_summary = translate_from_english(final_summary, output_lang)
+
+    docx_file = generate_docx(final_summary)
+    pdf_file = generate_pdf(final_summary)
+    return final_summary, ("summary.docx", docx_file), ("summary.pdf", pdf_file)
+
+# 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"]),
+        gr.Radio([
+            "Short (1–2 sentences)",
+            "Medium (3–5 sentences)",
+            "Detailed (paragraph)"
+        ], label="Summary length", value="Medium (3–5 sentences)"),
+        gr.Dropdown(["English", "Hindi", "Marathi"], label="Document Language", value="English"),
+        gr.Dropdown(["English", "Hindi", "Marathi"], label="Summary Output Language", value="English")
+    ],
+    outputs=[
+        gr.Textbox(label="Summary"),
+        gr.File(label="Download as DOCX"),
+        gr.File(label="Download as PDF")
+    ],
+    title="🌍 Multilingual Document Summarizer",
+    description="Upload or paste a document in English, Hindi, or Marathi. App will translate if needed and summarize it into your chosen output language."
+)
+
+iface.launch()

requirements.txt

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+transformers>=4.40.0
+torch>=2.1.0
+gradio>=4.26.0
+docx2txt==0.8
+PyPDF2>=3.0.1
+fpdf
+python-docx