app.py

import subprocess 

# Install required dependencies
subprocess.run(
    'pip install flash-attn --no-build-isolation',
    env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"},
    shell=True
)
subprocess.run('pip install googletrans==4.0.0-rc1 httpx>=0.24.1 gradio>=5.9.1 gradio-client>=1.5.2', shell=True)

import os
import re
import time
import torch
import spaces
import gradio as gr
from threading import Thread
from googletrans import Translator
from transformers import (
    AutoModelForCausalLM, 
    AutoTokenizer, 
    BitsAndBytesConfig, 
    TextIteratorStreamer
)

# Configuration Constants
MODEL_ID = "CohereForAI/aya-expanse-8b"
DEFAULT_SYSTEM_PROMPT = """ 
You are a bilingual translator specializing in Arabic and English translations. Your objective is to create precise, contextually appropriate, and stylistically consistent translations that adhere to the following guidelines:

Writing Style:

1. Grammar Accuracy: Always ensure translations are grammatically correct.

2. Contextual Suitability: Tailor translations to the context and audience:

Use concise, clear sentences for medical and legal content.

Employ engaging, imaginative language for marketing material.

Preserve formality and eloquence for legal content.

3. Proper Structure: Respect Arabic sentence structures and avoid direct replication of source language grammar. Favor "الجملة الفعلية" unless "الجملة الاسمية" is more suitable (e.g., for headlines or disclaimers).

Style Choices:

Use diacritics only when necessary for clarity.

Handle proper nouns and acronyms according to context:

Transliterate names and drug names unless an Arabic equivalent exists.

Translate program, department, and agency names when beneficial.

Use Arabic numerals and ensure proper handling of units, addresses, and references.

Punctuation:

Apply Arabic punctuation rules, ensuring proper readability.

Use the Arabic comma (،) and semicolon (؛) as per conventions.

Avoid excessive use of quotation marks and ensure logical placement of colons (:).

Common Mistakes to Avoid:

Avoid translating "is" as "is considered" unless contextually appropriate.

Correctly use prepositions and conjunctions for natural sentence flow.

Minimize repetitive structures; leverage pronouns where applicable.

Avoid overuse of constructions like "(قام + الفعل)" and "الخاص بـ."

Specific Terminology:

For legal translations, maintain formal tone and ensure accuracy in terminology.

For medical translations, simplify technical terms for lay audiences but retain complexity for professionals.

For marketing translations, prioritize creativity over literal translation, aligning with the core message.

Formatting Guidelines:

Consistently follow Arabic typographic standards.

Preserve the format of critical data (e.g., dates, measurements, and legal citations).

When in doubt, prioritize clarity, consistency, and alignment with the target audience's needs. Always reconcile project-specific instructions with these guidelines, giving precedence to client requirements when conflicts arise.
"""
# UI Configuration
TITLE = "<h1><center>Mawared T Assistant</center></h1>"
PLACEHOLDER = "Ask me anything! I'll think through it step by step."

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
.message-wrap {
    overflow-x: auto;
}
.message-wrap p {
    margin-bottom: 1em;
}
.message-wrap pre {
    background-color: #f6f8fa;
    border-radius: 3px;
    padding: 16px;
    overflow-x: auto;
}
.message-wrap code {
    background-color: rgba(175,184,193,0.2);
    border-radius: 3px;
    padding: 0.2em 0.4em;
    font-family: monospace;
}
.custom-tag {
    color: #0066cc;
    font-weight: bold;
}
.chat-area {
    height: 500px !important;
    overflow-y: auto !important;
}
"""

def initialize_model():
    """Initialize the model with appropriate configurations"""
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=True
    )

    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
    if tokenizer.pad_token_id is None:
        tokenizer.pad_token_id = tokenizer.eos_token_id

    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.float16,
        device_map="cuda",
        attn_implementation="flash_attention_2",
        #quantization_config=quantization_config

    )

    return model, tokenizer

def format_text(text):
    """Format text with proper spacing and tag highlighting (but keep tags visible)"""
    tag_patterns = [
        (r'<Thinking>', '\n<Thinking>\n'),
        (r'</Thinking>', '\n</Thinking>\n'),
        (r'<Critique>', '\n<Critique>\n'),
        (r'</Critique>', '\n</Critique>\n'),
        (r'<Revising>', '\n<Revising>\n'),
        (r'</Revising>', '\n</Revising>\n'),
        (r'<Final>', '\n<Final>\n'),
        (r'</Final>', '\n</Final>\n')
    ]
    
    formatted = text
    for pattern, replacement in tag_patterns:
        formatted = re.sub(pattern, replacement, formatted)
    
    formatted = '\n'.join(line for line in formatted.split('\n') if line.strip())
    
    return formatted

def format_chat_history(history):
    """Format chat history for display, keeping tags visible"""
    formatted = []
    for user_msg, assistant_msg in history:
        formatted.append(f"User: {user_msg}")
        if assistant_msg:
            formatted.append(f"Assistant: {assistant_msg}")
    return "\n\n".join(formatted)
    
def create_examples():
    """Create example queries for the UI"""
    return [
        "Explain the concept of artificial intelligence.",
        "How does photosynthesis work?",
        "What are the main causes of climate change?",
        "Describe the process of protein synthesis.",
        "What are the key features of a democratic government?",
        "Explain the theory of relativity.",
        "How do vaccines work to prevent diseases?",
        "What are the major events of World War II?",
        "Describe the structure of a human cell.",
        "What is the role of DNA in genetics?"
    ]

@spaces.GPU()
def chat_response(
    message: str,
    history: list,
    chat_display: str,
    system_prompt: str,
    temperature: float = 1.0,
    max_new_tokens: int = 8192,
    top_p: float = 0.8,
    top_k: int = 40,
    penalty: float = 1.2,
):
    """Generate chat responses, keeping tags visible in the output"""
    conversation = [
        {"role": "system", "content": system_prompt}
    ]
    
    for prompt, answer in history:
        conversation.extend([
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": answer}
        ])
    
    conversation.append({"role": "user", "content": message})
    
    input_ids = tokenizer.apply_chat_template(
        conversation,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to(model.device)
    
    streamer = TextIteratorStreamer(
        tokenizer,
        timeout=60.0,
        skip_prompt=True,
        skip_special_tokens=True
    )
    
    generate_kwargs = dict(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=False if temperature == 0 else True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        repetition_penalty=penalty,
        streamer=streamer,
    )
    
    buffer = ""
    
    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=generate_kwargs)
        thread.start()
        
        history = history + [[message, ""]]
        
        for new_text in streamer:
            buffer += new_text
            formatted_buffer = format_text(buffer)
            history[-1][1] = formatted_buffer
            chat_display = format_chat_history(history)
            
            yield history, chat_display

    # Translate the final response to Arabic
    translator = Translator()
    translated_text = translator.translate(buffer, src='en', dest='ar').text
    history[-1][1] = translated_text
    chat_display = format_chat_history(history)
    
    yield history, chat_display

def process_example(example: str) -> tuple:
    """Process example query and return empty history and updated display"""
    return [], f"User: {example}\n\n"

def main():
    """Main function to set up and launch the Gradio interface"""
    global model, tokenizer
    model, tokenizer = initialize_model()
    
    with gr.Blocks(css=CSS, theme="soft") as demo:
        gr.HTML(TITLE)
        gr.DuplicateButton(
            value="Duplicate Space for private use",
            elem_classes="duplicate-button"
        )
        
        with gr.Row():
            with gr.Column():
                chat_history = gr.State([])
                chat_display = gr.TextArea(
                    value="",
                    label="Chat History",
                    interactive=False,
                    elem_classes=["chat-area"],
                )
                
                message = gr.TextArea(
                    placeholder=PLACEHOLDER,
                    label="Your message",
                    lines=3
                )
                
                with gr.Row():
                    submit = gr.Button("Send")
                    clear = gr.Button("Clear")
                
                with gr.Accordion("⚙️ Advanced Settings", open=False):
                    system_prompt = gr.TextArea(
                        value=DEFAULT_SYSTEM_PROMPT,
                        label="System Prompt",
                        lines=5,
                    )
                    temperature = gr.Slider(
                        minimum=0,
                        maximum=1,
                        step=0.1,
                        value=0.2,
                        label="Temperature",
                    )
                    max_tokens = gr.Slider(
                        minimum=128,
                        maximum=32000,
                        step=128,
                        value=8192,
                        label="Max Tokens",
                    )
                    top_p = gr.Slider(
                        minimum=0.1,
                        maximum=1.0,
                        step=0.1,
                        value=0.8,
                        label="Top-p",
                    )
                    top_k = gr.Slider(
                        minimum=1,
                        maximum=100,
                        step=1,
                        value=40,
                        label="Top-k",
                    )
                    penalty = gr.Slider(
                        minimum=1.0,
                        maximum=2.0,
                        step=0.1,
                        value=1.2,
                        label="Repetition Penalty",
                    )
                
                examples = gr.Examples(
                    examples=create_examples(),
                    inputs=[message],
                    outputs=[chat_history, chat_display],
                    fn=process_example,
                    cache_examples=False,
                )
        
        # Set up event handlers
        submit_click = submit.click(
            chat_response,
            inputs=[
                message,
                chat_history,
                chat_display,
                system_prompt,
                temperature,
                max_tokens,
                top_p,
                top_k,
                penalty,
            ],
            outputs=[chat_history, chat_display],
            show_progress=True,
        )
        
        message.submit(
            chat_response,
            inputs=[
                message,
                chat_history,
                chat_display,
                system_prompt,
                temperature,
                max_tokens,
                top_p,
                top_k,
                penalty,
            ],
            outputs=[chat_history, chat_display],
            show_progress=True,
        )
        
        clear.click(
            lambda: ([], ""),
            outputs=[chat_history, chat_display],
            show_progress=True,
        )
        
        submit_click.then(lambda: "", outputs=message)
        message.submit(lambda: "", outputs=message)
    
    return demo

if __name__ == "__main__":
    demo = main()
    demo.launch()