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import os |
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import sys |
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import torch |
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import uvicorn |
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import redis |
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import numpy as np |
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from fastapi import FastAPI, Query, BackgroundTasks |
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from fastapi.responses import HTMLResponse |
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from starlette.middleware.cors import CORSMiddleware |
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from datasets import load_dataset |
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from transformers import AutoTokenizer, GPT2LMHeadModel, pipeline |
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from loguru import logger |
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from dotenv import load_dotenv |
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from sklearn.metrics.pairwise import cosine_similarity |
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sys.path.append('..') |
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load_dotenv() |
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huggingface_token = os.getenv('HUGGINGFACE_TOKEN') |
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kaggle_username = os.getenv('KAGGLE_USERNAME') |
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kaggle_key = os.getenv('KAGGLE_KEY') |
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redis_host = os.getenv('REDIS_HOST', 'localhost') |
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redis_port = os.getenv('REDIS_PORT', 6379) |
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redis_password = os.getenv('REDIS_PASSWORD', 'huggingface_spaces') |
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redis_client = redis.Redis(host=redis_host, port=redis_port, password=redis_password, decode_responses=True) |
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MAX_ITEMS_PER_TABLE = 10000 |
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def get_current_table_index(): |
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return int(redis_client.get("current_table_index") or 0) |
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def increment_table_index(): |
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current_index = get_current_table_index() |
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redis_client.set("current_table_index", current_index + 1) |
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def store_to_redis_table(key, content): |
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current_index = get_current_table_index() |
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table_name = f"table_{current_index}" |
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item_count = redis_client.hlen(table_name) |
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if item_count >= MAX_ITEMS_PER_TABLE: |
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increment_table_index() |
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table_name = f"table_{get_current_table_index()}" |
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redis_client.hset(table_name, key, content) |
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def load_and_store_models(model_names): |
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for name in model_names: |
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try: |
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model = GPT2LMHeadModel.from_pretrained(name) |
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tokenizer = AutoTokenizer.from_pretrained(name) |
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sample_text = "Sample input" |
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generated_text = model.generate(tokenizer.encode(sample_text, return_tensors="pt"), max_length=50) |
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decoded_text = tokenizer.decode(generated_text[0], skip_special_tokens=True) |
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store_to_redis_table(name, decoded_text) |
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redis_client.hset("models", name, decoded_text) |
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except Exception as e: |
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logger.error(f"Error loading model {name}: {e}") |
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app = FastAPI() |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=["*"], |
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allow_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"] |
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) |
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message_history = [] |
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@app.get('/') |
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async def index(): |
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chat_history = redis_client.hgetall(f"table_{get_current_table_index()}") |
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chat_history_html = "".join(f"<div class='bot-message'>{msg}</div>" for msg in chat_history.values()) |
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html_code = f""" |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>ChatGPT Chatbot</title> |
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<style> |
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body {{ font-family: Arial, sans-serif; margin: 0; padding: 0; background-color: #f4f4f4; }} |
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.container {{ max-width: 800px; margin: auto; padding: 20px; }} |
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.chat-container {{ background-color: #fff; border-radius: 8px; box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); overflow: hidden; margin-bottom: 20px; }} |
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.chat-box {{ height: 300px; overflow-y: auto; padding: 10px; }} |
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.chat-input {{ width: calc(100% - 20px); border: none; border-top: 1px solid #ddd; padding: 10px; font-size: 16px; outline: none; }} |
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.user-message, .bot-message {{ margin-bottom: 10px; padding: 8px 12px; border-radius: 8px; max-width: 70%; word-wrap: break-word; }} |
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.user-message {{ background-color: #007bff; color: #fff; align-self: flex-end; }} |
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.bot-message {{ background-color: #4CAF50; color: #fff; }} |
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</style> |
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</head> |
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<body> |
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<div class="container"> |
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<h1 style="text-align: center;">ChatGPT Chatbot</h1> |
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<div class="chat-container" id="chat-container"> |
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<div class="chat-box" id="chat-box"> |
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{chat_history_html} |
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</div> |
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<input type="text" class="chat-input" id="user-input" placeholder="Type your message..."> |
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</div> |
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</div> |
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<script> |
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const userInput = document.getElementById('user-input'); |
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userInput.addEventListener('keyup', function(event) {{ |
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if (event.key === 'Enter') {{ |
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event.preventDefault(); |
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sendMessage(); |
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}} |
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}}); |
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function sendMessage() {{ |
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const userMessage = userInput.value.trim(); |
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if (userMessage === '') return; |
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appendMessage('user', userMessage); |
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userInput.value = ''; |
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fetch(`/autocomplete?q=` + encodeURIComponent(userMessage)) |
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.then(response => response.json()) |
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.then(data => {{ |
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fetch(`/get_response?q=` + encodeURIComponent(userMessage)) |
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.then(response => response.json()) |
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.then(data => {{ |
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const botMessage = data.response; |
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appendMessage('bot', botMessage); |
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}}) |
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.catch(error => {{ |
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console.error('Error:', error); |
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}}); |
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}}) |
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.catch(error => {{ |
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console.error('Error:', error); |
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}}); |
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}} |
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function appendMessage(sender, message) {{ |
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const chatBox = document.getElementById('chat-box'); |
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const messageElement = document.createElement('div'); |
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messageElement.className = sender + '-message'; |
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messageElement.innerText = message; |
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chatBox.appendChild(messageElement); |
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}} |
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</script> |
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</body> |
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</html> |
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""" |
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return HTMLResponse(content=html_code, status_code=200) |
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def calculate_similarity(base_text, candidate_texts): |
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base_vector = np.array([len(base_text)]) |
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similarities = [] |
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for text in candidate_texts: |
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candidate_vector = np.array([len(text)]) |
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similarity = cosine_similarity([base_vector], [candidate_vector]) |
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similarities.append(similarity[0][0]) |
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return similarities |
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@app.get('/autocomplete') |
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async def autocomplete(q: str = Query(..., title='query'), background_tasks: BackgroundTasks): |
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global message_history |
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message_history.append(('user', q)) |
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background_tasks.add_task(generate_responses, q) |
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return {"status": "Processing request, please wait..."} |
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@app.get('/get_response') |
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async def get_response(q: str = Query(..., title='query')): |
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response = redis_client.hget("responses", q) |
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return {"response": response} |
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def generate_responses(q): |
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generated_responses = [] |
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try: |
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for model_name in redis_client.hkeys("models"): |
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try: |
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model_data = redis_client.hget("models", model_name) |
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model = GPT2LMHeadModel.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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text_generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1) |
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generated_response = text_generation_pipeline(q, do_sample=True, max_length=50, num_return_sequences=5) |
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generated_responses.extend([response['generated_text'] for response in generated_response]) |
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except Exception as e: |
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logger.error(f"Error generating response with model {model_name}: {e}") |
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if generated_responses: |
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similarities = calculate_similarity(q, generated_responses) |
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most_coherent_response = generated_responses[np.argmax(similarities)] |
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store_to_redis_table(q, "\n".join(generated_responses)) |
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redis_client.hset("responses", q, most_coherent_response) |
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else: |
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logger.warning("No valid responses generated.") |
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except Exception as e: |
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logger.error(f"General error in autocomplete: {e}") |
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if __name__ == '__main__': |
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gpt2_models = [ |
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"gpt2", |
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"gpt2-medium", |
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"gpt2-large", |
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"gpt2-xl" |
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] |
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programming_models = [ |
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"google/bert2bert_L-24_uncased", |
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"microsoft/CodeGPT-small-java", |
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"microsoft/CodeGPT-small-python", |
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"Salesforce/codegen-350M-multi" |
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] |
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load_and_store_models(gpt2_models + programming_models) |
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uvicorn.run(app=app, host='0.0.0.0', port=int(os.getenv("PORT", 8001))) |
