app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import spaces
import torch
from datasets import load_dataset
from huggingface_hub import CommitScheduler
from pathlib import Path
import uuid
import json
import time
from datetime import datetime
import logging


# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("app.log"),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger("darija-llm")

device = "cuda:0" if torch.cuda.is_available() else "cpu"
logger.info(f'Using device: {device}')

# token
token = os.environ['TOKEN']

# Load the pretrained model and tokenizer
MODEL_NAME = "atlasia/Al-Atlas-0.5B" # "atlasia/Al-Atlas-LLM-mid-training" # "BounharAbdelaziz/Al-Atlas-LLM-0.5B" #"atlasia/Al-Atlas-LLM"

logger.info(f"Loading model: {MODEL_NAME}")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME,token=token) # , token=token
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME,token=token).to(device)
logger.info("Model loaded successfully")

# Fix tokenizer padding
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token  # Set pad token
    logger.info("Set pad_token to eos_token")

# Predefined examples
examples = [
    ["الذكاء الاصطناعي هو فرع من علوم الكمبيوتر اللي كيركز"
     , 256, 0.7, 0.9, 150, 4, 1.5],
    ["المستقبل ديال الذكاء الصناعي فالمغرب"
     , 256, 0.7, 0.9, 150, 4, 1.5],
    [" المطبخ المغربي"
     , 256, 0.7, 0.9, 150, 4, 1.5],
    ["الماكلة المغربية كتعتبر من أحسن الماكلات فالعالم"
     , 256, 0.7, 0.9, 150, 4, 1.5],
]

# Define the file where to save the data
submit_file = Path("user_submit/") / f"data_{uuid.uuid4()}.json"
feedback_file = submit_file

# Create directory if it doesn't exist
submit_file.parent.mkdir(exist_ok=True, parents=True)
logger.info(f"Created feedback file: {feedback_file}")

scheduler = CommitScheduler(
    repo_id="atlasia/atlaset_inference_ds",
    repo_type="dataset",
    folder_path=submit_file.parent,
    path_in_repo="data",
    every=5,
    token=token
)
logger.info(f"Initialized CommitScheduler for repo: atlasia/atlaset_inference_ds")

# Track usage statistics
usage_stats = {
    "total_generations": 0,
    "total_tokens_generated": 0,
    "start_time": time.time()
}

@spaces.GPU
def generate_text(prompt, max_length=256, temperature=0.7, top_p=0.9, top_k=150, num_beams=8, repetition_penalty=1.5, progress=gr.Progress()):
    if not prompt.strip():
        logger.warning("Empty prompt submitted")
        return "", "الرجاء إدخال نص للتوليد (Please enter text to generate)"
    
    logger.info(f"Generating text for prompt: '{prompt[:50]}...' (length: {len(prompt)})")
    logger.info(f"Parameters: max_length={max_length}, temp={temperature}, top_p={top_p}, top_k={top_k}, beams={num_beams}, rep_penalty={repetition_penalty}")
    
    start_time = time.time()
    
    # Start progress
    progress(0, desc="تجهيز النموذج (Preparing model)")
    
    # Tokenize input
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    progress(0.1, desc="تحليل النص (Tokenizing)")
    
    # Generate text with optimized parameters for speed
    progress(0.2, desc="توليد النص (Generating text)")
    output = model.generate(
        **inputs, 
        max_length=max_length, 
        temperature=temperature, 
        top_p=top_p, 
        do_sample=True,
        repetition_penalty=repetition_penalty,
        num_beams=1 if num_beams > 4 else num_beams,  # Reduce beam search or use greedy decoding
        top_k=top_k,
        early_stopping=True,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
        use_cache=True,  # Ensure cache is used
    )
    
    # Decode output
    progress(0.9, desc="معالجة النتائج (Processing results)")
    result = tokenizer.decode(output[0], skip_special_tokens=True)
    
    # Update stats
    generation_time = time.time() - start_time
    token_count = len(output[0])
    
    with scheduler.lock:
        usage_stats["total_generations"] += 1
        usage_stats["total_tokens_generated"] += token_count
    
    logger.info(f"Generated {token_count} tokens in {generation_time:.2f}s")
    logger.info(f"Result: '{result[:50]}...' (length: {len(result)})")
    
    # Save feedback with additional metadata
    save_feedback(
        prompt, 
        result,
        {
            "max_length": max_length,
            "temperature": temperature,
            "top_p": top_p,
            "top_k": top_k,
            "num_beams": num_beams,
            "repetition_penalty": repetition_penalty,
            "generation_time": generation_time,
            "token_count": token_count,
            "timestamp": datetime.now().isoformat()
        }
    )
    
    progress(1.0, desc="اكتمل (Complete)")
    
    return result, f"تم توليد {token_count} رمز في {generation_time:.2f} ثانية (Generated {token_count} tokens in {generation_time:.2f} seconds)"

def save_feedback(input, output, params) -> None:
    """
    Append input/outputs and parameters to a JSON Lines file using a thread lock
    to avoid concurrent writes from different users.
    """
    logger.info(f"Saving feedback to {feedback_file}")
    
    with scheduler.lock:
        try:
            with feedback_file.open("a") as f:
                f.write(json.dumps({
                    "input": input, 
                    "output": output, 
                    "params": params
                }))
                f.write("\n")
            logger.info("Feedback saved successfully")
        except Exception as e:
            logger.error(f"Error saving feedback: {str(e)}")

def get_stats():
    """Return current usage statistics"""
    with scheduler.lock:
        uptime = time.time() - usage_stats["start_time"]
        hours = uptime / 3600
        
        stats = {
            "Total generations": usage_stats["total_generations"],
            "Total tokens generated": usage_stats["total_tokens_generated"],
            "Uptime": f"{int(hours)}h {int((hours % 1) * 60)}m",
            "Generations per hour": f"{usage_stats['total_generations'] / hours:.1f}" if hours > 0 else "N/A",
            "Last updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        }
        
        logger.info(f"Stats requested: {stats}")
        return stats

def reset_params():
    """Reset parameters to default values"""
    logger.info("Parameters reset to defaults")
    return 128, 0.7, 0.9, 50, 1, 1.2  # Updated defaults for faster generation

def thumbs_up_callback(input_text, output_text):
    """Record positive feedback"""
    logger.info("Received positive feedback")
    
    feedback_path = Path("user_submit") / "positive_feedback.jsonl"
    feedback_path.parent.mkdir(exist_ok=True, parents=True)
    
    with scheduler.lock:
        try:
            with feedback_path.open("a") as f:
                feedback_data = {
                    "input": input_text, 
                    "output": output_text, 
                    "rating": "positive",
                    "timestamp": datetime.now().isoformat()
                }
                f.write(json.dumps(feedback_data))
                f.write("\n")
            
            logger.info(f"Positive feedback saved to {feedback_path}")
        except Exception as e:
            logger.error(f"Error saving positive feedback: {str(e)}")
    
    return "شكرا على التقييم الإيجابي!"

def thumbs_down_callback(input_text, output_text, feedback=""):
    """Record negative feedback"""
    logger.info(f"Received negative feedback: '{feedback}'")
    
    feedback_path = Path("user_submit") / "negative_feedback.jsonl"
    feedback_path.parent.mkdir(exist_ok=True, parents=True)
    
    with scheduler.lock:
        try:
            with feedback_path.open("a") as f:
                feedback_data = {
                    "input": input_text, 
                    "output": output_text, 
                    "rating": "negative",
                    "feedback": feedback,
                    "timestamp": datetime.now().isoformat()
                }
                f.write(json.dumps(feedback_data))
                f.write("\n")
            
            logger.info(f"Negative feedback saved to {feedback_path}")
        except Exception as e:
            logger.error(f"Error saving negative feedback: {str(e)}")
    
    return "شكرا على ملاحظاتك!"
    
if __name__ == "__main__":
    logger.info("Starting Moroccan Darija LLM application")
    
    # Create the Gradio interface
    with gr.Blocks(css="footer {visibility: hidden}") as app:
        gr.Markdown("""
        # 🇲🇦 نموذج اللغة المغربية الدارجة (Moroccan Darija LLM)
        
        أدخل نصًا بالدارجة المغربية واحصل على نص تم إنشاؤه بواسطة نموذج اللغة الخاص بنا المدرب على الدارجة المغربية.
        
        Enter a prompt and get AI-generated text using our pretrained LLM on Moroccan Darija.
        """)
        
        with gr.Row():
            with gr.Column(scale=6):
                prompt_input = gr.Textbox(
                    label="الدخل (Prompt): دخل النص بالدارجة", 
                    placeholder="اكتب هنا...",
                    lines=4, rtl=True
                )
                
                with gr.Row():
                    submit_btn = gr.Button("توليد النص (Generate)", variant="primary")
                    clear_btn = gr.Button("مسح (Clear)")
                    reset_btn = gr.Button("إعادة ضبط المعلمات (Reset Parameters)")
                
                with gr.Accordion("معلمات التوليد (Generation Parameters)", open=False):
                    with gr.Row():
                        with gr.Column():
                            max_length = gr.Slider(8, 4096, value=128, label="Max Length (الطول الأقصى)")  # Reduced default
                            temperature = gr.Slider(0.0, 2, value=0.7, label="Temperature (درجة الحرارة)")
                            top_p = gr.Slider(0.0, 1.0, value=0.9, label="Top-p (أعلى احتمال)")
                        
                        with gr.Column():
                            top_k = gr.Slider(1, 10000, value=50, label="Top-k (أعلى ك)")  # Reduced default
                            num_beams = gr.Slider(1, 20, value=1, label="Number of Beams (عدد الأشعة)")  # Reduced default
                            repetition_penalty = gr.Slider(0.0, 100.0, value=1.2, label="Repetition Penalty (عقوبة التكرار)")  # Reduced default
                
            with gr.Column(scale=6):
                output_text = gr.Textbox(label="النص المولد (Generated Text)", lines=10, rtl=True)
                generation_info = gr.Markdown("")
                
                with gr.Row():
                    thumbs_up = gr.Button("👍 جيد")
                    thumbs_down = gr.Button("👎 سيء")
                
                with gr.Accordion("تعليق (Feedback)", open=False, visible=False) as feedback_accordion:
                    feedback_text = gr.Textbox(label="لماذا لم يعجبك الناتج؟ (Why didn't you like the output?)", lines=2, rtl=True)
                    submit_feedback = gr.Button("إرسال التعليق (Submit Feedback)")
                
                feedback_result = gr.Markdown("")
        
        with gr.Accordion("إحصائيات الاستخدام (Usage Statistics)", open=False):
            stats_md = gr.JSON(get_stats, every=10)
            refresh_stats = gr.Button("تحديث (Refresh)")
        
        # Examples section with caching
        gr.Examples(
            examples=examples,
            inputs=[prompt_input, max_length, temperature, top_p, top_k, num_beams, repetition_penalty],
            outputs=[output_text, generation_info],
            fn=generate_text,
            cache_examples=True
        )
        
        # Button actions
        submit_btn.click(
            generate_text,
            inputs=[prompt_input, max_length, temperature, top_p, top_k, num_beams, repetition_penalty],
            outputs=[output_text, generation_info]
        )
        
        clear_btn.click(
            lambda: ("", ""),
            inputs=None,
            outputs=[prompt_input, output_text]
        )
        
        reset_btn.click(
            reset_params,
            inputs=None,
            outputs=[max_length, temperature, top_p, top_k, num_beams, repetition_penalty]
        )
        
        # Feedback system
        thumbs_up.click(
            thumbs_up_callback,
            inputs=[prompt_input, output_text],
            outputs=[feedback_result]
        )
        
        thumbs_down.click(
            lambda: (gr.Accordion.update(visible=True, open=True), ""),
            inputs=None,
            outputs=[feedback_accordion, feedback_result]
        )
        
        submit_feedback.click(
            thumbs_down_callback,
            inputs=[prompt_input, output_text, feedback_text],
            outputs=[feedback_result]
        )
        
        # Stats refresh
        refresh_stats.click(
            get_stats,
            inputs=None,
            outputs=[stats_md]
        )
        
        # Keyboard shortcuts
        prompt_input.submit(
            generate_text,
            inputs=[prompt_input, max_length, temperature, top_p, top_k, num_beams, repetition_penalty],
            outputs=[output_text, generation_info]
        )
    
    logger.info("Launching Gradio interface")
    app.launch()
    logger.info("Gradio interface closed")