from transformers import pipeline

# Intent classification
classifier = pipeline("text-classification", model="facebook/bart-large-mnli")

def classify_intent(text):
    labels = ["greeting", "question", "request", "complaint"]
    result = classifier(text, candidate_labels=labels)
    return result['labels'][0]

class DialogManager:
    def __init__(self):
        self.context = {}
        self.conversation_history = []
    
    def update_context(self, user_input, intent):
        self.conversation_history.append({"user": user_input})
        self.context["last_intent"] = intent
    
    def get_response(self, intent):
        # Response selection based on intent and context
        pass

from transformers import pipeline

# Named Entity Recognition
ner = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")

def extract_entities(text):
    entities = ner(text)
    return [(ent['word'], ent['entity']) for ent in entities]

from transformers import AutoModelForCausalLM, AutoTokenizer

def generate_response(prompt, model, tokenizer):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

class ContextManager:
    def __init__(self):
        self.context = {}
        self.max_history = 5
        
    def update(self, user_input, bot_response):
        if len(self.context.get('history', [])) >= self.max_history:
            self.context['history'].pop(0)
        self.context.setdefault('history', []).append({
            'user': user_input,
            'bot': bot_response
        })

from transformers import pipeline

sentiment_analyzer = pipeline("sentiment-analysis")

def analyze_sentiment(text):
    result = sentiment_analyzer(text)[0]
    return {
        'sentiment': result['label'],
        'confidence': result['score']
    }

class ConversationHandler:
    def __init__(self):
        self.history = []
        self.context = {}
        
    def process_turn(self, user_input):
        # Update context
        self.history.append({"user": user_input})
        
        # Generate response
        response = self.generate_response(user_input)
        
        # Update history
        self.history.append({"bot": response})
        return response

def safe_response(func):
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            return {
                "status": "error",
                "message": "I'm having trouble understanding. Could you rephrase that?",
                "error": str(e)
            }
    return wrapper

response_templates = {
    "greeting": [
        "Hello! How can I help you today?",
        "Hi there! What can I do for you?",
        "Welcome! How may I assist you?"
    ],
    "fallback": [
        "I'm not sure I understand. Could you rephrase that?",
        "Could you please provide more details?",
        "I'm still learning. Could you try asking differently?"
    ]
}

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ChatInput(BaseModel):
    message: str
    session_id: str

@app.post("/chat")
async def chat_endpoint(chat_input: ChatInput):
    response = chatbot.process_message(
        chat_input.message,
        chat_input.session_id
    )
    return {"response": response}

@app.post("/webhook")
async def webhook_handler(request: Request):
    data = await request.json()
    
    # Process webhook data
    response = process_webhook_event(data)
    
    return response

import unittest

class ChatbotTests(unittest.TestCase):
    def setUp(self):
        self.chatbot = Chatbot()
    
    def test_greeting(self):
        response = self.chatbot.process_message("Hello")
        self.assertIn("greeting", self.chatbot.get_intent(response))

def test_conversation_flow():
    conversation = [
        ("Hi", "greeting"),
        ("What's the weather?", "weather_query"),
        ("Thank you", "gratitude")
    ]
    
    for user_input, expected_intent in conversation:
        intent = chatbot.classify_intent(user_input)
        assert intent == expected_intent

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def log_conversation(user_input, bot_response, metadata):
    logger.info({
        "user_input": user_input,
        "bot_response": bot_response,
        "timestamp": metadata.get("timestamp"),
        "session_id": metadata.get("session_id")
    })