Zeyad12
/

pegasus-large-summarizer

@@ -361,438 +361,6 @@ Epoch 4: Fine-tuned generation quality
 ---
-## API Documentation
-### Overview
-The PEGASUS Summarization API provides a RESTful interface for document summarization with comprehensive error handling, performance optimization, and flexible configuration options.
-### Base URL
-```
-http://localhost:5000
-```
-### Authentication
-Currently, no authentication is required. For production deployment, consider implementing API key authentication.
-### Endpoints
-#### 1. Health Check
-```http
-GET /health
-```
-**Response:**
-```json
-{
-  "status": "healthy",
-  "model_loaded": true,
-  "device": "cuda:0",
-  "timestamp": 1638360000.0
-}
-```
-#### 2. Model Information
-```http
-GET /model-info
-```
-**Response:**
-```json
-{
-  "model_name": "Fine-tuned PEGASUS",
-  "base_model": "google/pegasus-large",
-  "fine_tuned_on": "Scientific Papers Dataset (500 documents)",
-  "max_input_length": 1024,
-  "max_output_length": 512,
-  "device": "cuda:0",
-  "capabilities": {
-    "chunking": true,
-    "length_control": true,
-    "custom_parameters": true,
-    "batch_processing": false,
-    "streaming": false
-  }
-}
-```
-#### 3. Summarization
-```http
-POST /summarize
-```
-**Request Body:**
-```json
-{
-  "text": "Your document text here...",
-  "max_length": 200,
-  "config": {
-    "num_beams": 4,
-    "length_penalty": 2.0,
-    "temperature": 1.0,
-    "top_k": 50,
-    "top_p": 0.95
-  }
-}
-```
-**Response:**
-```json
-{
-  "summary": "Generated summary text...",
-  "input_length": 1250,
-  "input_tokens": 312,
-  "output_length": 180,
-  "output_tokens": 45,
-  "processing_time": 2.34,
-  "chunks_processed": 1,
-  "model_used": "fine-tuned-pegasus",
-  "success": true
-}
-```
-### Configuration Parameters
-| Parameter              | Type  | Range   | Default | Description                       |
-| ---------------------- | ----- | ------- | ------- | --------------------------------- |
-| `max_length`           | int   | 50-500  | 512     | Maximum summary length in tokens  |
-| `num_beams`            | int   | 1-8     | 4       | Beam search width for generation  |
-| `length_penalty`       | float | 0.5-3.0 | 2.0     | Penalty for sequence length       |
-| `temperature`          | float | 0.1-2.0 | 1.0     | Sampling temperature              |
-| `top_k`                | int   | 10-100  | 50      | Top-k sampling parameter          |
-| `top_p`                | float | 0.1-1.0 | 0.95    | Nucleus sampling parameter        |
-| `diversity_penalty`    | float | 0.0-2.0 | 0.5     | Diversity penalty for beam groups |
-| `no_repeat_ngram_size` | int   | 1-5     | 3       | Prevent n-gram repetition         |
-### Error Handling
-**HTTP Status Codes:**
-- `200`: Success
-- `400`: Bad Request (invalid parameters)
-- `404`: Endpoint not found
-- `405`: Method not allowed
-- `500`: Internal server error
-**Error Response Format:**
-```json
-{
-  "error": "Error description",
-  "success": false,
-  "details": "Additional error information"
-}
-```
-### Rate Limiting & Performance
-**Current Limitations:**
-- No rate limiting implemented (add for production)
-- Single request processing (no batch support)
-- Memory usage scales with document length
-**Performance Characteristics:**
-- Short documents (< 500 tokens): ~1-2 seconds
-- Medium documents (500-1000 tokens): ~2-4 seconds
-- Long documents (> 1000 tokens): ~4-8 seconds
----
-## Installation & Setup
-### Prerequisites
-**System Requirements:**
-- Python 3.8 or higher
-- 8GB RAM minimum (16GB recommended)
-- GPU with 6GB VRAM (optional but recommended)
-- 10GB free disk space
-**Dependencies:**
-- PyTorch 2.0+
-- Transformers 4.30+
-- Flask 2.3+
-- Other packages listed in requirements.txt
-### Installation Steps
-#### 1. Clone/Download the Project
-```powershell
-# Navigate to your desired directory
-cd "f:\University\GP Final\Summarization_Model"
-```
-#### 2. Create Virtual Environment
-```powershell
-# Create virtual environment
-python -m venv venv
-# Activate virtual environment
-.\venv\Scripts\Activate.ps1
-```
-#### 3. Install Dependencies
-```powershell
-# Install required packages
-pip install -r requirements.txt
-```
-#### 4. Verify Model Files
-Ensure the fine-tuned model is available:
-```
-Pegasus-Fine-Tuned/
-└── checkpoint-200/
-    ├── config.json
-    ├── model.safetensors
-    ├── tokenizer_config.json
-    └── ...
-```
-#### 5. Start the API Server
-```powershell
-# Start the Flask application
-python app.py
-```
-The server will start on `http://localhost:5000`
-### Docker Deployment (Optional)
-Create a `Dockerfile`:
-```dockerfile
-FROM python:3.9-slim
-WORKDIR /app
-COPY requirements.txt .
-RUN pip install -r requirements.txt
-COPY . .
-EXPOSE 5000
-CMD ["python", "app.py"]
-```
-Build and run:
-```powershell
-docker build -t pegasus-api .
-docker run -p 5000:5000 pegasus-api
-```
----
-## Usage Examples
-### 1. Basic Python Client
-```python
-import requests
-import json
-def summarize_text(text, max_length=200):
-    url = "http://localhost:5000/summarize"
-    payload = {
-        "text": text,
-        "max_length": max_length
-    }
-    response = requests.post(url, json=payload)
-    if response.status_code == 200:
-        result = response.json()
-        if result["success"]:
-            return result["summary"]
-        else:
-            print(f"Error: {result['error']}")
-    else:
-        print(f"HTTP Error: {response.status_code}")
-    return None
-# Example usage
-document = """
-Artificial intelligence and machine learning have transformed numerous industries
-in recent years. From healthcare to finance, these technologies are enabling
-automation and insights that were previously impossible. Deep learning, in
-particular, has shown remarkable success in computer vision, natural language
-processing, and speech recognition tasks.
-"""
-summary = summarize_text(document)
-print(f"Summary: {summary}")
-```
-### 2. Advanced Configuration
-```python
-def advanced_summarize(text):
-    url = "http://localhost:5000/summarize"
-    payload = {
-        "text": text,
-        "max_length": 150,
-        "config": {
-            "num_beams": 6,
-            "length_penalty": 1.5,
-            "temperature": 0.8,
-            "top_p": 0.9,
-            "diversity_penalty": 0.7
-        }
-    }
-    response = requests.post(url, json=payload)
-    return response.json()
-# More creative and diverse summaries
-result = advanced_summarize(document)
-print(f"Advanced Summary: {result['summary']}")
-print(f"Processing Time: {result['processing_time']:.2f}s")
-```
-### 3. Batch Processing
-```python
-def batch_summarize(documents, max_length=200):
-    """Process multiple documents sequentially"""
-    results = []
-    for i, doc in enumerate(documents):
-        print(f"Processing document {i+1}/{len(documents)}")
-        summary = summarize_text(doc, max_length)
-        results.append({
-            "document_id": i,
-            "original_length": len(doc),
-            "summary": summary
-        })
-    return results
-# Example with multiple documents
-documents = [
-    "Document 1 content...",
-    "Document 2 content...",
-    "Document 3 content..."
-]
-batch_results = batch_summarize(documents)
-```
-### 4. Error Handling
-```python
-def robust_summarize(text, max_retries=3):
-    """Summarize with retry logic and error handling"""
-    for attempt in range(max_retries):
-        try:
-            response = requests.post(
-                "http://localhost:5000/summarize",
-                json={"text": text},
-                timeout=30
-            )
-            if response.status_code == 200:
-                result = response.json()
-                if result["success"]:
-                    return result
-                else:
-                    print(f"API Error: {result['error']}")
-            else:
-                print(f"HTTP Error: {response.status_code}")
-        except requests.exceptions.Timeout:
-            print(f"Timeout on attempt {attempt + 1}")
-        except requests.exceptions.ConnectionError:
-            print(f"Connection error on attempt {attempt + 1}")
-        if attempt < max_retries - 1:
-            time.sleep(2 ** attempt)  # Exponential backoff
-    return None
-```
-### 5. Performance Monitoring
-```python
-def monitor_performance(text):
-    """Monitor and log performance metrics"""
-    import time
-    start_time = time.time()
-    result = summarize_text(text)
-    client_time = time.time() - start_time
-    if result:
-        server_time = result.get("processing_time", 0)
-        network_time = client_time - server_time
-        print(f"Performance Metrics:")
-        print(f"  Total Time: {client_time:.2f}s")
-        print(f"  Server Time: {server_time:.2f}s")
-        print(f"  Network Time: {network_time:.2f}s")
-        print(f"  Input Tokens: {result.get('input_tokens', 'N/A')}")
-        print(f"  Output Tokens: {result.get('output_tokens', 'N/A')}")
-        print(f"  Chunks Processed: {result.get('chunks_processed', 1)}")
-    return result
-```
-### 6. Integration with File Processing
-```python
-import os
-from pathlib import Path
-def process_text_files(directory_path, output_file="summaries.json"):
-    """Process all text files in a directory"""
-    results = []
-    directory = Path(directory_path)
-    for file_path in directory.glob("*.txt"):
-        with open(file_path, 'r', encoding='utf-8') as file:
-            content = file.read()
-        print(f"Processing: {file_path.name}")
-        summary = summarize_text(content)
-        results.append({
-            "filename": file_path.name,
-            "original_length": len(content),
-            "summary": summary,
-            "summary_length": len(summary) if summary else 0
-        })
-    # Save results
-    with open(output_file, 'w', encoding='utf-8') as f:
-        json.dump(results, f, indent=2, ensure_ascii=False)
-    print(f"Results saved to {output_file}")
-    return results
-# Process all text files in a directory
-results = process_text_files("./documents/")
-```
----
 ## Technical Specifications
 ### Model Architecture Details
@@ -847,41 +415,6 @@ Max Position Embeddings: 1024
 - Concurrent requests: Limited by memory (recommend 1-2 concurrent on 16GB RAM)
 - Daily capacity: ~1000-5000 documents (depends on length and hardware)
-### Scalability Considerations
-**Current Limitations:**
-1. Single-threaded processing
-2. No request queuing
-3. Memory usage scales with document length
-4. No horizontal scaling support
-**Recommended Improvements for Production:**
-1. Implement request queuing with Redis/RabbitMQ
-2. Add horizontal scaling with load balancer
-3. Implement caching for repeated requests
-4. Add batch processing capabilities
-5. Optimize memory usage with model quantization
-### Security Considerations
-**Current Security Features:**
-- Input validation and sanitization
-- Error message filtering
-- Request size limits
-**Production Security Recommendations:**
-1. **API Authentication**: Implement JWT or API key authentication
-2. **Rate Limiting**: Prevent abuse with request rate limits
-3. **Input Validation**: Comprehensive input sanitization
-4. **HTTPS**: Use SSL/TLS encryption
-5. **Monitoring**: Log all requests and monitor for anomalies
-6. **Network Security**: Use firewalls and VPNs for internal access
----
 ## Comparison: Before vs After Fine-tuning
@@ -1074,262 +607,6 @@ Epoch 4: 2.489 (best model selected)
 ---
-## Troubleshooting
-### Common Issues and Solutions
-#### 1. Model Loading Issues
-**Problem**: Model fails to load or takes too long
-```
-Error: "Failed to load any PEGASUS model"
-```
-**Solutions:**
-```powershell
-# Check if model files exist
-ls "Pegasus-Fine-Tuned\checkpoint-200\"
-# Verify file integrity
-# Re-extract checkpoint if corrupted
-Expand-Archive -Path "Pegasus-Fine-Tuned\checkpoint-200.zip" -DestinationPath "." -Force
-# Check available memory
-Get-WmiObject -Class Win32_ComputerSystem | Select-Object TotalPhysicalMemory
-# Free up memory if needed
-[System.GC]::Collect()
-```
-#### 2. CUDA/GPU Issues
-**Problem**: GPU not detected or CUDA errors
-```
-Error: "CUDA out of memory" or "CUDA device not available"
-```
-**Solutions:**
-```python
-# Check CUDA availability
-import torch
-print(f"CUDA available: {torch.cuda.is_available()}")
-print(f"CUDA devices: {torch.cuda.device_count()}")
-# Clear GPU cache
-torch.cuda.empty_cache()
-# Force CPU usage if needed
-device = torch.device("cpu")
-```
-#### 3. Memory Issues
-**Problem**: Out of memory errors during processing
-```
-Error: "RuntimeError: CUDA out of memory"
-```
-**Solutions:**
-1. **Reduce batch size**: Set `batch_size = 1` in config
-2. **Enable gradient checkpointing**: Add to training args
-3. **Use CPU fallback**: Force CPU processing for large documents
-4. **Implement chunking**: Process documents in smaller pieces
-```python
-# Memory-efficient processing
-def process_large_document(text):
-    # Split into smaller chunks
-    chunks = chunk_text(text, max_chunk_length=500)
-    summaries = []
-    for chunk in chunks:
-        summary = summarize_chunk(chunk)
-        summaries.append(summary)
-        # Clear cache after each chunk
-        torch.cuda.empty_cache()
-    return combine_summaries(summaries)
-```
-#### 4. API Connection Issues
-**Problem**: Cannot connect to API or timeouts
-**Solutions:**
-```powershell
-# Check if server is running
-netstat -an | findstr :5000
-# Test basic connectivity
-curl http://localhost:5000/health
-# Check firewall settings
-netsh advfirewall firewall show rule name="Python"
-# Restart server with verbose logging
-python app.py --debug
-```
-#### 5. Performance Issues
-**Problem**: Slow response times or high resource usage
-**Optimization Strategies:**
-```python
-# 1. Optimize generation parameters
-config = {
-    "num_beams": 2,  # Reduce from 4
-    "max_length": 256,  # Reduce if appropriate
-    "early_stopping": True
-}
-# 2. Implement caching
-from functools import lru_cache
-@lru_cache(maxsize=100)
-def cached_summarize(text_hash):
-    return summarize(text)
-# 3. Use model quantization
-from transformers import AutoModelForSeq2SeqLM
-model = AutoModelForSeq2SeqLM.from_pretrained(
-    model_path,
-    torch_dtype=torch.float16  # Use half precision
-)
-```
-#### 6. Text Processing Issues
-**Problem**: Poor quality summaries or encoding errors
-**Solutions:**
-```python
-# Text preprocessing improvements
-def robust_preprocess(text):
-    # Handle encoding issues
-    if isinstance(text, bytes):
-        text = text.decode('utf-8', errors='ignore')
-    # Remove problematic characters
-    text = re.sub(r'[^\x00-\x7F]+', ' ', text)
-    # Normalize whitespace
-    text = re.sub(r'\s+', ' ', text)
-    # Validate minimum length
-    if len(text.split()) < 10:
-        raise ValueError("Text too short for summarization")
-    return text.strip()
-```
-### Performance Debugging
-#### Monitoring Tools
-**1. GPU Monitoring:**
-```powershell
-# Install NVIDIA monitoring tools
-nvidia-smi
-# Continuous monitoring
-nvidia-smi -l 1
-```
-**2. Memory Profiling:**
-```python
-import psutil
-import GPUtil
-def monitor_resources():
-    # CPU and RAM
-    cpu_percent = psutil.cpu_percent()
-    memory = psutil.virtual_memory()
-    # GPU
-    gpus = GPUtil.getGPUs()
-    if gpus:
-        gpu = gpus[0]
-        gpu_memory = f"{gpu.memoryUsed}/{gpu.memoryTotal} MB"
-        gpu_util = f"{gpu.load * 100:.1f}%"
-    print(f"CPU: {cpu_percent}%")
-    print(f"RAM: {memory.percent}%")
-    print(f"GPU Memory: {gpu_memory}")
-    print(f"GPU Utilization: {gpu_util}")
-```
-**3. Request Timing:**
-```python
-import time
-from functools import wraps
-def timing_decorator(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        start = time.time()
-        result = func(*args, **kwargs)
-        end = time.time()
-        print(f"{func.__name__} took {end - start:.2f} seconds")
-        return result
-    return wrapper
-@timing_decorator
-def summarize_with_timing(text):
-    return summarize(text)
-```
-### Deployment Issues
-#### Production Deployment Checklist
-**1. Environment Setup:**
-- [ ] Python version compatibility (3.8+)
-- [ ] All dependencies installed
-- [ ] Model files accessible
-- [ ] Sufficient memory available
-- [ ] GPU drivers updated (if using GPU)
-**2. Security Configuration:**
-- [ ] API authentication implemented
-- [ ] Input validation enabled
-- [ ] Rate limiting configured
-- [ ] HTTPS enabled
-- [ ] Firewall rules set
-**3. Performance Optimization:**
-- [ ] Model quantization applied
-- [ ] Caching implemented
-- [ ] Request queuing configured
-- [ ] Load balancing set up
-- [ ] Monitoring tools deployed
-**4. Error Handling:**
-- [ ] Comprehensive logging enabled
-- [ ] Error tracking configured
-- [ ] Graceful degradation implemented
-- [ ] Health checks operational
-- [ ] Backup systems ready
----
 ## Conclusion
 This PEGASUS Fine-tuned Document Summarization System represents a significant advancement in domain-specific text summarization. Through careful fine-tuning on scientific papers, the model demonstrates substantial improvements in accuracy, coherence, and domain-appropriate language usage.

 ---
 ## Technical Specifications
 ### Model Architecture Details
 - Concurrent requests: Limited by memory (recommend 1-2 concurrent on 16GB RAM)
 - Daily capacity: ~1000-5000 documents (depends on length and hardware)
 ## Comparison: Before vs After Fine-tuning
 ---
 ## Conclusion
 This PEGASUS Fine-tuned Document Summarization System represents a significant advancement in domain-specific text summarization. Through careful fine-tuning on scientific papers, the model demonstrates substantial improvements in accuracy, coherence, and domain-appropriate language usage.