Advanced Gradio Integration

Gradio Interface
    ├── UI Components (Blocks API)
    │   ├── Chatbot widget
    │   ├── Textbox input
    │   └── Action buttons
    ├── State Management
    │   ├── Conversation history
    │   └── Task state
    ├── Text Processing
    │   ├── Markdown rendering (mdtex2html)
    │   └── Code highlighting
    └── Model Integration
        ├── Streaming generation
        └── Memory management

task_history = [
    ("User message 1", "Assistant response 1"),
    ("User message 2", "Assistant response 2"),
    # ...
]

def predict_with_system(_query, _system_prompt, _chatbot, _task_history):
    # Prepend system prompt to history
    if _system_prompt and len(_task_history) == 0:
        _task_history.append((f"<system>{_system_prompt}</system>", "Understood."))
    
    # Continue with normal prediction
    for response in model.chat_stream(tokenizer, _query, history=_task_history):
        # ...

system_prompt = gr.Textbox(label="System Prompt", placeholder="You are a helpful assistant...")

models = {
    "Qwen-7B-Chat": load_model("Qwen/Qwen-7B-Chat"),
    "Qwen-14B-Chat": load_model("Qwen/Qwen-14B-Chat"),
}

def predict_multi_model(_query, _model_name, _chatbot, _task_history):
    model = models[_model_name]
    # Use selected model for generation

model_dropdown = gr.Dropdown(
    choices=["Qwen-7B-Chat", "Qwen-14B-Chat"],
    value="Qwen-7B-Chat",
    label="Model"
)

with gr.Accordion("Generation Settings", open=False):
    temperature = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
    top_p = gr.Slider(0.1, 1.0, value=0.8, step=0.05, label="Top-p")
    max_tokens = gr.Slider(128, 4096, value=2048, step=128, label="Max Tokens")

def predict_with_config(_query, _temp, _top_p, _max_tokens, _chatbot, _task_history):
    # Update generation config
    config = GenerationConfig(
        temperature=_temp,
        top_p=_top_p,
        max_new_tokens=_max_tokens
    )
    for response in model.chat_stream(tokenizer, _query, history=_task_history, generation_config=config):
        # ...

import json
from datetime import datetime

def export_conversation(_task_history):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"qwen_chat_{timestamp}.json"
    
    data = {
        "timestamp": timestamp,
        "conversation": [
            {"role": "user", "content": user_msg}
            {"role": "assistant", "content": bot_msg}
            for user_msg, bot_msg in _task_history
        ]
    }
    
    with open(filename, 'w', encoding='utf-8') as f:
        json.dump(data, f, ensure_ascii=False, indent=2)
    
    return filename

export_btn = gr.Button("💾 Export")
export_btn.click(export_conversation, [task_history], [gr.File()])

# Cache models globally
_model_cache = {}

def get_model(checkpoint_path):
    if checkpoint_path not in _model_cache:
        model = AutoModelForCausalLM.from_pretrained(
            checkpoint_path,
            device_map="auto",
            trust_remote_code=True
        ).eval()
        _model_cache[checkpoint_path] = model
    return _model_cache[checkpoint_path]

import torch
import gc

def aggressive_cleanup():
    """Thorough memory cleanup"""
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

# Call after clearing history or long conversations
empty_btn.click(lambda: aggressive_cleanup(), None, None)

from functools import lru_cache
import hashlib

response_cache = {}

def get_cache_key(query, history):
    content = query + str(history)
    return hashlib.md5(content.encode()).hexdigest()

def predict_with_cache(_query, _chatbot, _task_history):
    cache_key = get_cache_key(_query, _task_history)
    
    if cache_key in response_cache:
        response = response_cache[cache_key]
        _chatbot.append((_parse_text(_query), _parse_text(response)))
        return _chatbot
    
    # Normal generation...
    response_cache[cache_key] = full_response

demo.queue(
    concurrency_count=4,  # Process up to 4 requests simultaneously
    max_size=20,          # Queue up to 20 requests
).launch(
    # launch options...
)

def predict_safe(_query, _chatbot, _task_history):
    try:
        for response in model.chat_stream(tokenizer, _query, history=_task_history):
            _chatbot[-1] = (_parse_text(_query), _parse_text(response))
            yield _chatbot
    except Exception as e:
        error_msg = f"Error: {str(e)}"
        _chatbot[-1] = (_parse_text(_query), error_msg)
        yield _chatbot
        print(f"Error in generation: {e}")

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('qwen_demo.log'),
        logging.StreamHandler()
    ]
)

def predict_with_logging(_query, _chatbot, _task_history):
    logging.info(f"User query: {_query}")
    
    start_time = time.time()
    for response in model.chat_stream(...):
        # ...
    
    duration = time.time() - start_time
    logging.info(f"Generation completed in {duration:.2f}s")

from collections import defaultdict
import time

user_requests = defaultdict(list)
RATE_LIMIT = 10  # requests per minute

def check_rate_limit(user_id):
    now = time.time()
    # Remove old requests
    user_requests[user_id] = [
        t for t in user_requests[user_id] 
        if now - t < 60
    ]
    
    if len(user_requests[user_id]) >= RATE_LIMIT:
        return False
    
    user_requests[user_id].append(now)
    return True

def health_check():
    try:
        # Simple model test
        test_response = model.chat(tokenizer, "Hi", history=None)[0]
        return "✓ Healthy"
    except Exception as e:
        return f"✗ Unhealthy: {e}"

health_status = gr.Textbox(label="System Health", interactive=False)
health_btn = gr.Button("Check Health")
health_btn.click(health_check, None, health_status)

demo.queue().launch(
    auth=[('admin', 'password123'), ('user', 'userpass')],
    auth_message="Enter credentials to access Qwen Chat",
    server_port=8000,
)

import analytics

def predict_with_analytics(_query, _chatbot, _task_history):
    # Track usage
    analytics.track('chat_message', {
        'query_length': len(_query),
        'history_length': len(_task_history)
    })
    
    # Normal prediction
    yield from predict(_query, _chatbot, _task_history)

import sqlite3

def save_conversation(_task_history, user_id):
    conn = sqlite3.connect('conversations.db')
    cursor = conn.cursor()
    
    for user_msg, bot_msg in _task_history:
        cursor.execute(
            'INSERT INTO messages (user_id, role, content) VALUES (?, ?, ?)',
            (user_id, 'user', user_msg)
        )
        cursor.execute(
            'INSERT INTO messages (user_id, role, content) VALUES (?, ?, ?)',
            (user_id, 'assistant', bot_msg)
        )
    
    conn.commit()
    conn.close()