Update Summarization/app.py

Summarization/app.py

@@ -1,145 +1,3 @@
-# app.py
-"""from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-import fitz, docx, pptx, openpyxl, re, nltk, tempfile, os, easyocr, datetime, hashlib
-from nltk.tokenize import sent_tokenize
-from fpdf import FPDF
-from gtts import gTTS
-
-nltk.download('punkt', quiet=True)
-
-# Load models
-MODEL_NAME = "facebook/bart-large-cnn"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
-model.eval()
-summarizer = pipeline("summarization", model=model, tokenizer=tokenizer, device=-1, batch_size=4)
-reader = easyocr.Reader(['en'], gpu=False)
-
-summary_cache = {}
-
-def clean_text(text: str) -> str:
-    text = re.sub(r'\s+', ' ', text)
-    text = re.sub(r'\u2022\s*|\d\.\s+', '', text)
-    text = re.sub(r'\[.*?\]|\(.*?\)', '', text)
-    text = re.sub(r'\bPage\s*\d+\b', '', text, flags=re.IGNORECASE)
-    return text.strip()
-
-def extract_text(file_path: str, ext: str):
-    try:
-        if ext == "pdf":
-            with fitz.open(file_path) as doc:
-                text = "\n".join(page.get_text("text") for page in doc)
-                if len(text.strip()) < 50:
-                    images = [page.get_pixmap() for page in doc]
-                    temp_img = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-                    images[0].save(temp_img.name)
-                    text = "\n".join(reader.readtext(temp_img.name, detail=0))
-                    os.unlink(temp_img.name)
-        elif ext == "docx":
-            doc = docx.Document(file_path)
-            text = "\n".join(p.text for p in doc.paragraphs)
-        elif ext == "pptx":
-            prs = pptx.Presentation(file_path)
-            text = "\n".join(shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text"))
-        elif ext == "xlsx":
-            wb = openpyxl.load_workbook(file_path, read_only=True)
-            text = "\n".join([" ".join(str(cell) for cell in row if cell) for sheet in wb.sheetnames for row in wb[sheet].iter_rows(values_only=True)])
-        else:
-            text = ""
-    except Exception as e:
-        return "", f"Error extracting text: {str(e)}"
-
-    return clean_text(text), ""
-
-def chunk_text(text: str, max_tokens: int = 950):
-    sentences = sent_tokenize(text)
-    chunks, current_chunk = [], ""
-    for sentence in sentences:
-        if len(tokenizer.encode(current_chunk + " " + sentence)) <= max_tokens:
-            current_chunk += " " + sentence
-        else:
-            chunks.append(current_chunk.strip())
-            current_chunk = sentence
-    if current_chunk:
-        chunks.append(current_chunk.strip())
-    return chunks
-
-def generate_summary(text: str, length: str = "medium"):
-    cache_key = hashlib.md5((text + length).encode()).hexdigest()
-    if cache_key in summary_cache:
-        return summary_cache[cache_key]
-
-    length_params = {
-        "short": {"max_length": 80, "min_length": 30},
-        "medium": {"max_length": 200, "min_length": 80},
-        "long": {"max_length": 300, "min_length": 210}
-    }
-
-    chunks = chunk_text(text)
-    summaries = summarizer(
-        chunks,
-        max_length=length_params[length]["max_length"],
-        min_length=length_params[length]["min_length"],
-        do_sample=False,
-        truncation=True,
-        no_repeat_ngram_size=2,
-        num_beams=2,
-        early_stopping=True
-    )
-    final_summary = " ".join(s['summary_text'] for s in summaries)
-    final_summary = ". ".join(s.strip().capitalize() for s in final_summary.split(". ") if s.strip())
-    final_summary = final_summary if len(final_summary) > 25 else "Summary too short."
-
-    summary_cache[cache_key] = final_summary
-    return final_summary
-
-def text_to_speech(text: str):
-    try:
-        tts = gTTS(text)
-        temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
-        tts.save(temp_audio.name)
-        return temp_audio.name
-    except:
-        return ""
-
-def create_pdf(summary: str, filename: str):
-    try:
-        pdf = FPDF()
-        pdf.add_page()
-        pdf.set_font("Arial", size=12)
-        pdf.multi_cell(0, 10, summary)
-        temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
-        pdf.output(temp_pdf.name)
-        return temp_pdf.name
-    except:
-        return ""
-
-async def summarize_document(file, length="medium"):
-    contents = await file.read()
-    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-        tmp_file.write(contents)
-        tmp_path = tmp_file.name
-
-    ext = file.filename.split('.')[-1].lower()
-    text, error = extract_text(tmp_path, ext)
-
-    if error:
-        raise Exception(error)
-
-    if not text or len(text.split()) < 30:
-        raise Exception("Document too short to summarize.")
-
-    summary = generate_summary(text, length)
-    audio_path = text_to_speech(summary)
-    pdf_path = create_pdf(summary, file.filename)
-
-    result = {"summary": summary}
-    if audio_path:
-        result["audioUrl"] = f"/files/{os.path.basename(audio_path)}"
-    if pdf_path:
-        result["pdfUrl"] = f"/files/{os.path.basename(pdf_path)}"
-    return result"""
-# app.py
 
 from fastapi import UploadFile, File
 from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM