Update app.py

app.py

@@ -1,5 +1,5 @@
 # app.py
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+"""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
@@ -138,4 +138,211 @@ async def summarize_document(file, length="medium"):
         result["audioUrl"] = f"/files/{os.path.basename(audio_path)}"
     if pdf_path:
         result["pdfUrl"] = f"/files/{os.path.basename(pdf_path)}"
-    return result
+    return result"""
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import RedirectResponse, FileResponse, JSONResponse
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import fitz  # PyMuPDF
+import docx
+import pptx
+import openpyxl
+import re
+import nltk
+import torch
+from nltk.tokenize import sent_tokenize
+from gtts import gTTS
+from fpdf import FPDF
+import tempfile
+import os
+import easyocr
+import datetime
+import hashlib
+
+# Initialize
+nltk.download('punkt', quiet=True)
+app = FastAPI()
+
+# Load Summarizer Model
+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)
+
+# Load OCR Reader
+reader = easyocr.Reader(['en'], gpu=torch.cuda.is_available())
+
+# Cache
+summary_cache = {}
+
+# --- Helper Functions ---
+
+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, file_extension: str):
+    try:
+        if file_extension == "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)
+                    ocr_result = reader.readtext(temp_img.name, detail=0)
+                    os.unlink(temp_img.name)
+                    text = "\n".join(ocr_result) if ocr_result else text
+                return clean_text(text), ""
+
+        elif file_extension == "docx":
+            doc = docx.Document(file_path)
+            return clean_text("\n".join(p.text for p in doc.paragraphs)), ""
+
+        elif file_extension == "pptx":
+            prs = pptx.Presentation(file_path)
+            text = [shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text")]
+            return clean_text("\n".join(text)), ""
+
+        elif file_extension == "xlsx":
+            wb = openpyxl.load_workbook(file_path, read_only=True)
+            text = [" ".join(str(cell) for cell in row if cell) for sheet in wb.sheetnames for row in wb[sheet].iter_rows(values_only=True)]
+            return clean_text("\n".join(text)), ""
+
+        elif file_extension in ["jpg", "jpeg", "png"]:
+            ocr_result = reader.readtext(file_path, detail=0)
+            return clean_text("\n".join(ocr_result)), ""
+
+        return "", "Unsupported file format"
+    except Exception as e:
+        return "", f"Error reading {file_extension.upper()} file: {str(e)}"
+
+def chunk_text(text: str, max_tokens: int = 950):
+    try:
+        sentences = sent_tokenize(text)
+    except:
+        words = text.split()
+        sentences = [' '.join(words[i:i+20]) for i in range(0, len(words), 20)]
+
+    chunks = []
+    current_chunk = ""
+    for sentence in sentences:
+        token_length = len(tokenizer.encode(current_chunk + " " + sentence))
+        if token_length <= max_tokens:
+            current_chunk += " " + sentence
+        else:
+            if current_chunk.strip():
+                chunks.append(current_chunk.strip())
+            current_chunk = sentence
+
+    if current_chunk.strip():
+        chunks.append(current_chunk.strip())
+
+    return chunks
+
+def generate_summary(text: str, length: str = "medium") -> str:
+    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
+    )
+    summary_texts = [s['summary_text'] for s in summaries]
+
+    final_summary = " ".join(summary_texts)
+    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 - document may be too brief"
+
+    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 Exception:
+        return ""
+
+def create_pdf(summary: str, original_filename: str):
+    try:
+        pdf = FPDF()
+        pdf.add_page()
+        pdf.set_font("Arial", 'B', 16)
+        pdf.cell(200, 10, txt="Document Summary", ln=1, align='C')
+        pdf.set_font("Arial", size=12)
+        pdf.cell(200, 10, txt=f"Original file: {original_filename}", ln=1)
+        pdf.cell(200, 10, txt=f"Generated on: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=1)
+        pdf.ln(10)
+        pdf.multi_cell(0, 10, txt=summary)
+        temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+        pdf.output(temp_pdf.name)
+        return temp_pdf.name
+    except Exception:
+        return ""
+
+# --- API Endpoints ---
+
+@app.post("/summarize/")
+async def summarize_api(file: UploadFile = File(...), length: str = Form("medium")):
+    try:
+        contents = await file.read()
+        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+            tmp_file.write(contents)
+            tmp_path = tmp_file.name
+
+        file_ext = tmp_path.split('.')[-1].lower()
+        text, error = extract_text(tmp_path, file_ext)
+
+        if error:
+            return JSONResponse({"detail": error}, status_code=400)
+
+        if not text or len(text.split()) < 30:
+            return JSONResponse({"detail": "Document too short to summarize"}, status_code=400)
+
+        summary = generate_summary(text, length)
+        audio_path = text_to_speech(summary)
+        pdf_path = create_pdf(summary, file.filename)
+
+        response = {"summary": summary}
+        if audio_path:
+            response["audioUrl"] = f"/files/{os.path.basename(audio_path)}"
+        if pdf_path:
+            response["pdfUrl"] = f"/files/{os.path.basename(pdf_path)}"
+
+        return JSONResponse(response)
+
+    except Exception as e:
+        print(f"Error during summarization: {str(e)}")
+        return JSONResponse({"detail": f"Internal server error: {str(e)}"}, status_code=500)
+
+@app.get("/files/{file_name}")
+async def serve_file(file_name: str):
+    path = os.path.join(tempfile.gettempdir(), file_name)
+    if os.path.exists(path):
+        return FileResponse(path)
+    return JSONResponse({"error": "File not found"}, status_code=404)
+
+@app.get("/")
+def home():
+    return RedirectResponse(url="/")
+