Conversational

import os
from opperai import Opper

opper = Opper(api_key=os.getenv("OPPER_API_KEY"))

def generate_response(user_message: str) -> str:
    """Generate a conversational response to a user message."""
    response, _ = opper.call(
        name="chat_response",
        instructions="You are a helpful AI assistant. Respond to the user's message in a friendly and helpful manner.",
        input={"message": user_message},
        output_type=str
    )
    return response

def main():
    while True:
        user_input = input("User: ")
        if user_input.lower() in ['quit', 'exit', 'bye']:
            print("Assistant: Goodbye! Have a great day!")
            break
        
        response = generate_response(user_input)
        print(f"Assistant: {response}")

if __name__ == "__main__":
    main()

import os
from typing import List, Dict
from opperai import Opper

opper = Opper(api_key=os.getenv("OPPER_API_KEY"))

class ConversationManager:
    def __init__(self):
        self.messages: List[Dict[str, str]] = []
    
    def add_message(self, role: str, content: str):
        """Add a message to the conversation history."""
        self.messages.append({"role": role, "content": content})
    
    def generate_response(self, user_message: str) -> str:
        """Generate a response based on the full conversation context."""
        # Add the user's message to the conversation
        self.add_message("user", user_message)
        
        response, _ = opper.call(
            name="conversational_response",
            instructions="You are a helpful AI assistant. Use the conversation history to provide contextually relevant responses. Be conversational and engaging.",
            input={"messages": self.messages},
            output_type=str
        )
        
        # Add the assistant's response to the conversation
        self.add_message("assistant", response)
        return response
    
    def get_conversation_summary(self) -> str:
        """Get a summary of the conversation so far."""
        if not self.messages:
            return "No conversation history yet."
        
        summary, _ = opper.call(
            name="conversation_summary",
            instructions="Provide a brief summary of the conversation so far, highlighting key topics and user needs.",
            input={"messages": self.messages},
            output_type=str
        )
        return summary

def main():
    conversation = ConversationManager()
    
    print("Assistant: Hello! I'm your AI assistant. How can I help you today?")
    conversation.add_message("assistant", "Hello! I'm your AI assistant. How can I help you today?")
    
    while True:
        user_input = input("User: ")
        if user_input.lower() in ['quit', 'exit', 'bye']:
            print("Assistant: Goodbye! It was nice chatting with you!")
            break
        elif user_input.lower() == 'summary':
            summary = conversation.get_conversation_summary()
            print(f"Assistant: Here's a summary of our conversation:\n{summary}")
            continue
        
        response = conversation.generate_response(user_input)
        print(f"Assistant: {response}")

if __name__ == "__main__":
    main()

import os
from typing import Literal, Optional
from pydantic import BaseModel
from opperai import Opper

opper = Opper(api_key=os.getenv("OPPER_API_KEY"))

# Intent classification for understanding user requests
class IntentClassification(BaseModel):
    intent: Literal["greeting", "weather_inquiry", "help_request", "goodbye", "unknown"]
    confidence: float
    requires_location: bool = False

# Weather data structure
class WeatherInfo(BaseModel):
    location: str
    temperature: str
    condition: str
    humidity: str

# Mock weather database
weather_data = {
    "New York": {"temperature": "72°F", "condition": "Sunny", "humidity": "45%"},
    "London": {"temperature": "15°C", "condition": "Cloudy", "humidity": "70%"},
    "Tokyo": {"temperature": "25°C", "condition": "Rainy", "humidity": "80%"},
}

def classify_intent(user_message: str) -> IntentClassification:
    """Classify the user's intent from their message."""
    intent, _ = opper.call(
        name="intent_classifier",
        instructions="Classify the user's intent. Supported intents: greeting, weather_inquiry, help_request, goodbye, unknown. Set requires_location=True if the intent needs a location.",
        input={"message": user_message},
        output_type=IntentClassification
    )
    return intent

def extract_location(user_message: str) -> Optional[str]:
    """Extract location from user message."""
    location, _ = opper.call(
        name="location_extractor",
        instructions="Extract a location name from the user's message. Return only the location name or None if no location is found.",
        input={"message": user_message},
        output_type=str
    )
    return location if location and location.lower() != "none" else None

def get_weather(location: str) -> Optional[WeatherInfo]:
    """Get weather information for a location."""
    if location in weather_data:
        data = weather_data[location]
        return WeatherInfo(
            location=location,
            temperature=data["temperature"],
            condition=data["condition"],
            humidity=data["humidity"]
        )
    return None

def generate_response(user_message: str, conversation_history: list = None) -> str:
    """Generate a contextual response based on intent and conversation history."""
    if conversation_history is None:
        conversation_history = []
    
    # Classify the user's intent
    intent = classify_intent(user_message)
    
    # Handle different intents
    if intent.intent == "weather_inquiry":
        location = extract_location(user_message)
        if location:
            weather = get_weather(location)
            if weather:
                context = f"Weather for {location}: {weather.temperature}, {weather.condition}, Humidity: {weather.humidity}"
            else:
                context = f"Sorry, I don't have weather data for {location}"
        else:
            context = "I can help you with weather information. Please provide a location."
    elif intent.intent == "greeting":
        context = "User greeted me"
    elif intent.intent == "help_request":
        context = "User is asking for help"
    elif intent.intent == "goodbye":
        context = "User is saying goodbye"
    else:
        context = "Unknown intent"
    
    # Generate response with context
    response, _ = opper.call(
        name="contextual_response",
        instructions="Generate a helpful and natural response based on the user's intent and context. Be conversational and engaging.",
        input={
            "user_message": user_message,
            "intent": intent.intent,
            "context": context,
            "conversation_history": conversation_history
        },
        output_type=str
    )
    
    return response

def main():
    conversation_history = []
    
    print("Assistant: Hello! I'm your AI assistant. I can help you with weather information and general questions. How can I assist you today?")
    
    while True:
        user_input = input("User: ")
        if user_input.lower() in ['quit', 'exit', 'bye']:
            print("Assistant: Goodbye! Have a wonderful day!")
            break
        
        response = generate_response(user_input, conversation_history)
        print(f"Assistant: {response}")
        
        # Update conversation history
        conversation_history.append({"role": "user", "content": user_input})
        conversation_history.append({"role": "assistant", "content": response})

if __name__ == "__main__":
    main()