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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import logging
import torch
logger = logging.getLogger(__name__)
# Global chatbot components
chatbot_pipeline = None
chat_history = {}
def load_chatbot_model():
"""Load the free DialoGPT model for chatbot"""
global chatbot_pipeline
try:
logger.info("Loading DialoGPT chatbot model...")
# Use DialoGPT medium for better responses
chatbot_pipeline = pipeline(
"text-generation",
model="microsoft/DialoGPT-medium",
tokenizer="microsoft/DialoGPT-medium",
device="cpu"
)
logger.info("✓ DialoGPT chatbot model loaded successfully")
except Exception as e:
logger.error(f"✗ Failed to load DialoGPT model: {str(e)}")
chatbot_pipeline = None
async def get_chatbot_response(user_text: str, user_id: str = "default") -> str:
"""
Generate chatbot response using free DialoGPT model.
Args:
user_text: User input text
user_id: Unique user ID for maintaining conversation history
Returns:
Chatbot response text
"""
global chatbot_pipeline
try:
if chatbot_pipeline is None:
load_chatbot_model()
if chatbot_pipeline is None:
return get_fallback_response(user_text)
logger.info(f"Generating chatbot response for: '{user_text}'")
# Get or initialize chat history for this user
if user_id not in chat_history:
chat_history[user_id] = []
# Prepare conversation context
conversation = chat_history[user_id] + [user_text]
context = " ".join(conversation[-3:]) # Use last 3 exchanges as context
# Generate response
response = chatbot_pipeline(
context,
max_length=150,
num_return_sequences=1,
pad_token_id=chatbot_pipeline.tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=0.7
)
bot_response = response[0]['generated_text'].strip()
# Extract only the new response (remove the input context)
if context in bot_response:
bot_response = bot_response.replace(context, "").strip()
# Clean up the response
bot_response = clean_response(bot_response)
# Update chat history
chat_history[user_id].extend([user_text, bot_response])
# Keep only recent history (last 4 exchanges)
if len(chat_history[user_id]) > 8:
chat_history[user_id] = chat_history[user_id][-8:]
logger.info(f"✓ Response generated: '{bot_response}'")
return bot_response
except Exception as e:
logger.error(f"✗ Chatbot response failed: {str(e)}")
return get_fallback_response(user_text)
def clean_response(response: str) -> str:
"""Clean and format the chatbot response"""
# Remove extra spaces
response = ' '.join(response.split())
# Ensure proper sentence ending
if response and not response.endswith(('.', '!', '?')):
response += '.'
# Limit response length
if len(response) > 200:
response = response[:197] + '...'
return response
def get_fallback_response(user_text: str) -> str:
"""Provide fallback responses when model fails"""
fallback_responses = [
f"I understand you said: '{user_text}'. Could you tell me more about that?",
f"That's interesting! You mentioned: '{user_text}'. What would you like to know?",
f"Thanks for sharing! Regarding '{user_text}', how can I help you?",
f"I heard you say: '{user_text}'. Could you elaborate on that?"
]
import random
return random.choice(fallback_responses) |