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app.py

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+import os
+import pdfplumber
+from PIL import Image
+import pytesseract
+import numpy as np
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import transformers  # Full import for logging
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer, BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
+from datasets import load_dataset, concatenate_datasets
+import torch
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS for frontend compatibility
+UPLOAD_FOLDER = os.path.join(os.getcwd(), 'uploads')
+PEGASUS_MODEL_DIR = 'fine_tuned_pegasus'
+BERT_MODEL_DIR = 'fine_tuned_bert'
+LEGALBERT_MODEL_DIR = 'fine_tuned_legalbert'
+MAX_FILE_SIZE = 100 * 1024 * 1024  # 100 MB limit
+
+# Ensure upload folder exists
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+transformers.logging.set_verbosity_error()  # Suppress transformers warnings
+os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1"
+
+# Pegasus Fine-Tuning
+def load_or_finetune_pegasus():
+    if os.path.exists(PEGASUS_MODEL_DIR):
+        print("Loading fine-tuned Pegasus model...")
+        tokenizer = PegasusTokenizer.from_pretrained(PEGASUS_MODEL_DIR)
+        model = PegasusForConditionalGeneration.from_pretrained(PEGASUS_MODEL_DIR)
+    else:
+        print("Fine-tuning Pegasus on CNN/Daily Mail and XSUM...")
+        tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
+        model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
+        
+        cnn_dm = load_dataset("cnn_dailymail", "3.0.0", split="train[:5000]")
+        xsum = load_dataset("xsum", split="train[:5000]")
+        combined_dataset = concatenate_datasets([cnn_dm, xsum])
+        
+        def preprocess_function(examples):
+            inputs = tokenizer(examples["article"] if "article" in examples else examples["document"], 
+                              max_length=512, truncation=True, padding="max_length")
+            targets = tokenizer(examples["highlights"] if "highlights" in examples else examples["summary"], 
+                               max_length=400, truncation=True, padding="max_length")
+            inputs["labels"] = targets["input_ids"]
+            return inputs
+        
+        tokenized_dataset = combined_dataset.map(preprocess_function, batched=True)
+        train_dataset = tokenized_dataset.select(range(8000))
+        eval_dataset = tokenized_dataset.select(range(8000, 10000))
+        
+        training_args = TrainingArguments(
+            output_dir="./pegasus_finetune",
+            num_train_epochs=3,
+            per_device_train_batch_size=1,
+            per_device_eval_batch_size=1,
+            warmup_steps=500,
+            weight_decay=0.01,
+            logging_dir="./logs",
+            logging_steps=10,
+            eval_strategy="epoch",
+            save_strategy="epoch",
+            load_best_model_at_end=True,
+        )
+        
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+        )
+        
+        trainer.train()
+        trainer.save_model(PEGASUS_MODEL_DIR)
+        tokenizer.save_pretrained(PEGASUS_MODEL_DIR)
+        print(f"Fine-tuned Pegasus saved to {PEGASUS_MODEL_DIR}")
+    
+    return tokenizer, model
+
+# BERT Fine-Tuning
+def load_or_finetune_bert():
+    if os.path.exists(BERT_MODEL_DIR):
+        print("Loading fine-tuned BERT model...")
+        tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_DIR)
+        model = BertForSequenceClassification.from_pretrained(BERT_MODEL_DIR, num_labels=2)
+    else:
+        print("Fine-tuning BERT on CNN/Daily Mail for extractive summarization...")
+        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+        model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
+        
+        cnn_dm = load_dataset("cnn_dailymail", "3.0.0", split="train[:5000]")
+        
+        def preprocess_for_extractive(examples):
+            sentences = []
+            labels = []
+            for article, highlights in zip(examples["article"], examples["highlights"]):
+                article_sents = article.split(". ")
+                highlight_sents = highlights.split(". ")
+                for sent in article_sents:
+                    if sent.strip():
+                        is_summary = any(sent.strip() in h for h in highlight_sents)
+                        sentences.append(sent)
+                        labels.append(1 if is_summary else 0)
+            return {"sentence": sentences, "label": labels}
+        
+        dataset = cnn_dm.map(preprocess_for_extractive, batched=True, remove_columns=["article", "highlights", "id"])
+        tokenized_dataset = dataset.map(
+            lambda x: tokenizer(x["sentence"], max_length=512, truncation=True, padding="max_length"),
+            batched=True
+        )
+        tokenized_dataset = tokenized_dataset.remove_columns(["sentence"])
+        train_dataset = tokenized_dataset.select(range(int(0.8 * len(tokenized_dataset))))
+        eval_dataset = tokenized_dataset.select(range(int(0.8 * len(tokenized_dataset)), len(tokenized_dataset)))
+        
+        training_args = TrainingArguments(
+            output_dir="./bert_finetune",
+            num_train_epochs=3,
+            per_device_train_batch_size=8,
+            per_device_eval_batch_size=8,
+            warmup_steps=500,
+            weight_decay=0.01,
+            logging_dir="./logs",
+            logging_steps=10,
+            eval_strategy="epoch",
+            save_strategy="epoch",
+            load_best_model_at_end=True,
+        )
+        
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+        )
+        
+        trainer.train()
+        trainer.save_model(BERT_MODEL_DIR)
+        tokenizer.save_pretrained(BERT_MODEL_DIR)
+        print(f"Fine-tuned BERT saved to {BERT_MODEL_DIR}")
+    
+    return tokenizer, model
+
+# LegalBERT Fine-Tuning
+def load_or_finetune_legalbert():
+    if os.path.exists(LEGALBERT_MODEL_DIR):
+        print("Loading fine-tuned LegalBERT model...")
+        tokenizer = BertTokenizer.from_pretrained(LEGALBERT_MODEL_DIR)
+        model = BertForSequenceClassification.from_pretrained(LEGALBERT_MODEL_DIR, num_labels=2)
+    else:
+        print("Fine-tuning LegalBERT on Billsum for extractive summarization...")
+        tokenizer = BertTokenizer.from_pretrained("nlpaueb/legal-bert-base-uncased")
+        model = BertForSequenceClassification.from_pretrained("nlpaueb/legal-bert-base-uncased", num_labels=2)
+        
+        billsum = load_dataset("billsum", split="train[:5000]")
+        
+        def preprocess_for_extractive(examples):
+            sentences = []
+            labels = []
+            for text, summary in zip(examples["text"], examples["summary"]):
+                text_sents = text.split(". ")
+                summary_sents = summary.split(". ")
+                for sent in text_sents:
+                    if sent.strip():
+                        is_summary = any(sent.strip() in s for s in summary_sents)
+                        sentences.append(sent)
+                        labels.append(1 if is_summary else 0)
+            return {"sentence": sentences, "label": labels}
+        
+        dataset = billsum.map(preprocess_for_extractive, batched=True, remove_columns=["text", "summary", "title"])
+        tokenized_dataset = dataset.map(
+            lambda x: tokenizer(x["sentence"], max_length=512, truncation=True, padding="max_length"),
+            batched=True
+        )
+        tokenized_dataset = tokenized_dataset.remove_columns(["sentence"])
+        train_dataset = tokenized_dataset.select(range(int(0.8 * len(tokenized_dataset))))
+        eval_dataset = tokenized_dataset.select(range(int(0.8 * len(tokenized_dataset)), len(tokenized_dataset)))
+        
+        training_args = TrainingArguments(
+            output_dir="./legalbert_finetune",
+            num_train_epochs=3,
+            per_device_train_batch_size=8,
+            per_device_eval_batch_size=8,
+            warmup_steps=500,
+            weight_decay=0.01,
+            logging_dir="./logs",
+            logging_steps=10,
+            eval_strategy="epoch",
+            save_strategy="epoch",
+            load_best_model_at_end=True,
+        )
+        
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+        )
+        
+        trainer.train()
+        trainer.save_model(LEGALBERT_MODEL_DIR)
+        tokenizer.save_pretrained(LEGALBERT_MODEL_DIR)
+        print(f"Fine-tuned LegalBERT saved to {LEGALBERT_MODEL_DIR}")
+    
+    return tokenizer, model
+
+# Load models
+pegasus_tokenizer, pegasus_model = load_or_finetune_pegasus()
+bert_tokenizer, bert_model = load_or_finetune_bert()
+legalbert_tokenizer, legalbert_model = load_or_finetune_legalbert()
+
+def extract_text_from_pdf(file_path):
+    text = ""
+    with pdfplumber.open(file_path) as pdf:
+        for page in pdf.pages:
+            text += page.extract_text() or ""
+    return text
+
+def extract_text_from_image(file_path):
+    image = Image.open(file_path)
+    text = pytesseract.image_to_string(image)
+    return text
+
+def choose_model(text):
+    legal_keywords = ["court", "legal", "law", "judgment", "contract", "statute", "case"]
+    tfidf = TfidfVectorizer(vocabulary=legal_keywords)
+    tfidf_matrix = tfidf.fit_transform([text.lower()])
+    score = np.sum(tfidf_matrix.toarray())
+    if score > 0.1:
+        return "legalbert"
+    elif len(text.split()) > 50:
+        return "pegasus"
+    else:
+        return "bert"
+
+def summarize_with_pegasus(text):
+    inputs = pegasus_tokenizer(text, truncation=True, padding="longest", return_tensors="pt", max_length=512)
+    summary_ids = pegasus_model.generate(
+        inputs["input_ids"], 
+        max_length=400, min_length=80, length_penalty=1.5, num_beams=4
+    )
+    return pegasus_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+def summarize_with_bert(text):
+    sentences = text.split(". ")
+    if len(sentences) < 6:
+        return text
+    inputs = bert_tokenizer(sentences, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = bert_model(**inputs)
+    logits = outputs.logits
+    probs = torch.softmax(logits, dim=1)[:, 1]
+    key_sentence_idx = probs.argsort(descending=True)[:5]
+    return ". ".join([sentences[idx] for idx in key_sentence_idx if sentences[idx].strip()])
+
+def summarize_with_legalbert(text):
+    sentences = text.split(". ")
+    if len(sentences) < 6:
+        return text
+    inputs = legalbert_tokenizer(sentences, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = legalbert_model(**inputs)
+    logits = outputs.logits
+    probs = torch.softmax(logits, dim=1)[:, 1]
+    key_sentence_idx = probs.argsort(descending=True)[:5]
+    return ". ".join([sentences[idx] for idx in key_sentence_idx if sentences[idx].strip()])
+
+@app.route('/summarize', methods=['POST'])
+def summarize_document():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+    
+    file = request.files['file']
+    filename = file.filename
+    file.seek(0, os.SEEK_END)
+    file_size = file.tell()
+    if file_size > MAX_FILE_SIZE:
+        return jsonify({"error": f"File size exceeds {MAX_FILE_SIZE // (1024 * 1024)} MB"}), 413
+    file.seek(0)
+    file_path = os.path.join(UPLOAD_FOLDER, filename)
+    try:
+        file.save(file_path)
+    except Exception as e:
+        return jsonify({"error": f"Failed to save file: {str(e)}"}), 500
+
+    try:
+        if filename.endswith('.pdf'):
+            text = extract_text_from_pdf(file_path)
+        elif filename.endswith(('.png', '.jpeg', '.jpg')):
+            text = extract_text_from_image(file_path)
+        else:
+            os.remove(file_path)
+            return jsonify({"error": "Unsupported file format."}), 400
+    except Exception as e:
+        os.remove(file_path)
+        return jsonify({"error": f"Text extraction failed: {str(e)}"}), 500
+
+    if not text.strip():
+        os.remove(file_path)
+        return jsonify({"error": "No text extracted"}), 400
+
+    try:
+        model = choose_model(text)
+        if model == "pegasus":
+            summary = summarize_with_pegasus(text)
+        elif model == "bert":
+            summary = summarize_with_bert(text)
+        elif model == "legalbert":
+            summary = summarize_with_legalbert(text)
+    except Exception as e:
+        os.remove(file_path)
+        return jsonify({"error": f"Summarization failed: {str(e)}"}), 500
+
+    os.remove(file_path)
+    return jsonify({"model_used": model, "summary": summary})
+
+if __name__ == '__main__':
+    port = int(os.environ.get("PORT", 5000))  # Use PORT env var if set by Hugging Face
+    app.run(debug=False, host='0.0.0.0', port=port)

dockerfile

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+# Use an official Python runtime as the base image
+FROM python:3.8-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies for pdfplumber, pytesseract, and general compatibility
+RUN apt-get update && apt-get install -y \
+    tesseract-ocr \
+    libtesseract-dev \
+    poppler-utils \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy application code
+COPY . /app
+
+# Install Python dependencies, including sentencepiece for Pegasus
+RUN pip install --no-cache-dir \
+    flask \
+    flask-cors \
+    pdfplumber \
+    pillow \
+    pytesseract \
+    numpy \
+    torch \
+    transformers \
+    datasets \
+    scikit-learn \
+    gunicorn \
+    sentencepiece
+
+# Create uploads and cache directories with proper permissions
+RUN mkdir -p /app/uploads /app/cache && \
+    chmod -R 777 /app/uploads /app/cache
+
+# Set environment variable for Hugging Face cache (using HF_HOME as per latest transformers recommendation)
+ENV HF_HOME=/app/cache
+
+# Expose port (Hugging Face Spaces typically uses 7860, but we'll stick to 5000 and adjust in app.py if needed)
+EXPOSE 5000
+
+# Run with Gunicorn
+CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]

requirements.txt

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+flask
+flask-cors
+pdfplumber
+pillow
+pytesseract
+numpy
+torch
+transformers
+datasets
+scikit-learn
+gunicorn
+sentencepiece