', unsafe_allow_html=True)
st.metric("Risk Probability", f"{max(proba)*100:.1f}%")
st.markdown('
', unsafe_allow_html=True)
with r_col2:
st.markdown('', unsafe_allow_html=True)
if prediction == 1:
st.metric("Predicted Classification", "VIOLENT", delta="High Risk", delta_color="inverse")
else:
st.metric("Predicted Classification", "NON-VIOLENT", delta="Low Risk", delta_color="normal")
st.markdown('
', unsafe_allow_html=True)
with r_col3:
st.markdown('', unsafe_allow_html=True)
st.metric("Location Cluster", f"Zone {processed_df['LocationCluster'][0]}")
st.markdown('
', unsafe_allow_html=True)
# AI Analyst Report
st.markdown("### ๐ค AI Analyst Report")
risk_level = "CRITICAL" if proba[1] > 0.7 else "ELEVATED" if proba[1] > 0.4 else "STANDARD"
report = f"""
[CLASSIFIED REPORT - GENERATED BY AI]
-------------------------------------
DATE: {date_input} | TIME: {time_input}
LOCATION: {district} (Lat: {latitude}, Lon: {longitude})
ASSESSMENT: {risk_level} RISK DETECTED
PROBABILITY OF VIOLENCE: {proba[1]*100:.2f}%
KEY FACTORS:
- Time of Day: {time_input.hour}:00 hours (Historical high-risk window)
- District Profile: {district} shows elevated activity trends.
- Seasonal Context: {get_season(datetime_combined.month)} patterns observed.
RECOMMENDATION:
Immediate deployment of patrol units advised if risk > 50%.
Monitor sector {processed_df['LocationCluster'][0]} closely.
"""
st.markdown(f'", unsafe_allow_html=True)
st.subheader("๐ฌ AI Crime Safety Assistant")
st.markdown("Ask me anything about crime prediction, safety tips, or how this system works!", unsafe_allow_html=True)
# Initialize chat history in session state
if 'messages' not in st.session_state:
st.session_state.messages = [
{"role": "assistant", "content": "๐ Hello! I'm your AI Crime Safety Assistant. I can help you understand crime patterns, provide safety recommendations, and explain how our prediction model works. What would you like to know?"}
]
# Display chat history
st.markdown("
", unsafe_allow_html=True)
for message in st.session_state.messages:
if message["role"] == "user":
st.markdown(f"
๐ง {message['content']}
", unsafe_allow_html=True)
else:
st.markdown(f"
๐ค {message['content']}
", unsafe_allow_html=True)
st.markdown("
", unsafe_allow_html=True)
# Chat input
col1, col2 = st.columns([5, 1])
with col1:
user_input = st.text_input("Type your message...", key="chat_input", label_visibility="collapsed", placeholder="Ask about crime safety, predictions, or get recommendations...")
with col2:
send_button = st.button("Send ๐ค", use_container_width=True)
# Handle chat submission
if send_button and user_input:
# Add user message to history
st.session_state.messages.append({"role": "user", "content": user_input})
# Get AI response using Groq
with st.spinner("๐ง Thinking..."):
try:
client = get_groq_client()
# Create system prompt for crime prediction context
system_prompt = """You are an AI Crime Safety Assistant for a crime prediction system.
You help users understand:
- Crime patterns and trends in San Francisco
- How the XGBoost machine learning model predicts crime categories
- Safety tips and recommendations based on location and time
- What factors influence crime predictions (time, location, historical data)
Be helpful, concise, and informative. Keep responses to 2-3 sentences unless more detail is needed.
If asked about the model, explain it uses features like latitude, longitude, time, district, and description to predict crime types."""
# Prepare messages for Groq API
api_messages = [{"role": "system", "content": system_prompt}]
# Add recent chat history (last 5 messages for context)
for msg in st.session_state.messages[-5:]:
api_messages.append({"role": msg["role"], "content": msg["content"]})
# Get response from Groq
chat_completion = client.chat.completions.create(
messages=api_messages,
model="llama-3.3-70b-versatile",
temperature=0.7,
max_tokens=500
)
ai_response = chat_completion.choices[0].message.content
# Add AI response to history
st.session_state.messages.append({"role": "assistant", "content": ai_response})
except Exception as e:
error_msg = f"โ ๏ธ Sorry, I encountered an error: {str(e)}"
st.session_state.messages.append({"role": "assistant", "content": error_msg})
# Rerun to update chat display
st.rerun()
st.markdown("