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import streamlit as st
from typing import Dict
from datetime import datetime
import anthropic
from src.utils.session import get_tutor_context
from src.utils.config import get_anthropic_api_key
class AITutorService:
def __init__(self):
self.initialize_client()
self.load_avatar_assets()
def initialize_client(self):
"""Initialize Anthropic client"""
self.client = anthropic.Anthropic(api_key=get_anthropic_api_key())
def load_avatar_assets(self):
"""Load avatar assets and animations"""
self.avatar_states = {
'neutral': """
<svg width="100" height="100" viewBox="0 0 100 100">
<circle cx="50" cy="50" r="45" fill="#4A90E2"/>
<circle cx="35" cy="40" r="5" fill="white"/>
<circle cx="65" cy="40" r="5" fill="white"/>
<path d="M 30 60 Q 50 70 70 60" stroke="white" fill="none" stroke-width="3"/>
</svg>
""",
'thinking': """
<svg width="100" height="100" viewBox="0 0 100 100">
<circle cx="50" cy="50" r="45" fill="#4A90E2"/>
<circle cx="35" cy="40" r="5" fill="white"/>
<circle cx="65" cy="40" r="5" fill="white"/>
<path d="M 30 65 Q 50 65 70 65" stroke="white" fill="none" stroke-width="3"/>
</svg>
""",
'happy': """
<svg width="100" height="100" viewBox="0 0 100 100">
<circle cx="50" cy="50" r="45" fill="#4A90E2"/>
<circle cx="35" cy="40" r="5" fill="white"/>
<circle cx="65" cy="40" r="5" fill="white"/>
<path d="M 30 60 Q 50 80 70 60" stroke="white" fill="none" stroke-width="3"/>
</svg>
"""
}
def display_avatar(self, state: str = 'neutral'):
"""Display the AI tutor avatar"""
st.markdown(f"""
<div style="display: flex; justify-content: center; margin: 20px 0;">
{self.avatar_states.get(state, self.avatar_states['neutral'])}
</div>
""", unsafe_allow_html=True)
def generate_response(self, user_input: str) -> str:
"""Generate contextualized response using Claude"""
context = get_tutor_context()
prompt = self.build_prompt(user_input, context)
# Generate response using Claude
message = self.client.messages.create(
model="claude-3-opus-20240229",
max_tokens=1024,
temperature=0.7,
system="You are an expert AI tutor, skilled at explaining complex concepts clearly and engaging students in their learning journey. Provide detailed, accurate explanations while maintaining an encouraging tone.",
messages=[
{
"role": "user",
"content": prompt
}
]
)
response = message.content[0].text
# Analyze sentiment (Claude can do this as part of its response)
sentiment_score = 0.8 if any(word in response.lower() for word in ['great', 'excellent', 'good job', 'well done']) else 0.5
# Update engagement metrics
self.update_engagement_metrics(user_input, response, sentiment_score)
return response
def build_prompt(self, user_input: str, context: Dict) -> str:
"""Build a context-aware prompt for Claude"""
topic_context = f"Current topic: {context['current_topic']}\n" if context['current_topic'] else ""
chat_context = "\n".join([
f"Student: {msg['content']}" if msg['role'] == 'user' else f"Tutor: {msg['content']}"
for msg in context['chat_history'][-3:]
])
return f"""
You are an educational AI tutor helping a student learn.
{topic_context}
Difficulty level: {context['difficulty_level']}
Learning style: {context['learning_style']}
Previous conversation:
{chat_context}
Student: {user_input}
Provide a clear, helpful response that:
1. Addresses the student's question directly
2. Explains concepts in an engaging way
3. Uses examples when appropriate
4. Encourages deeper understanding
5. Maintains a supportive tone
If the question is about code, include working code examples.
If the topic is complex, break it down into simpler parts.
"""
def update_engagement_metrics(self, user_input: str, response: str, sentiment_score: float):
"""Update student engagement metrics"""
context = get_tutor_context()
context['engagement_metrics'].append({
'timestamp': datetime.now().isoformat(),
'sentiment_score': sentiment_score,
'interaction_length': len(user_input)
}) |