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import streamlit as st
import json
import time
from dotenv import load_dotenv
load_dotenv()
from crew_ai.chatbot import run_user_profile_retrieval, run_recommendations, run_crisis_check, run_condition_classification
from crew_ai.config import get_config
config = get_config()
def full_chat_flow(contextual_query: str, user_query: str, user_id: str = "anon_user"):
with st.chat_message("user"):
st.write(st.session_state.chat_history[-1]["content"])
with st.chat_message("assistant"):
st.write("📄 Processing your query...")
# Retrieve user profile
if st.session_state.user_profile is None:
st.session_state.user_profile = run_user_profile_retrieval(user_query, user_id)
crisis_result = run_crisis_check(contextual_query)
is_crisis = crisis_result.get("is_crisis", False)
explanation = crisis_result.get("explanation", "")
# Store crisis detection in session state
st.session_state.crisis_detected = is_crisis
st.session_state.crisis_explanation = explanation
if is_crisis:
with st.chat_message("assistant"):
st.error(f"🚨 Crisis detected: {explanation}")
st.info("We're prioritizing your safety. Connecting you with the best support now...")
rec = run_recommendations(
contextual_query,
json.dumps(st.session_state.user_profile),
condition="Crisis",
answers="{}",
interpretation="N/A",
is_crisis="true"
)
# task_outputs = rec.tasks_output
# retrieved_docs_crisis = task_outputs[0]
with st.chat_message("assistant"):
st.write("🆘 Crisis Support Recommendation:")
def stream_answer():
for word in rec["recommendation"].split(" "):
yield word + " "
time.sleep(0.1)
st.write_stream(stream_answer)
return rec
# return {
# "recommendation": rec["recommendation"],
# "condition": "crisis",
# "is_crisis": True,
# "crisis_explanation": explanation,
# "score_interpretation": "Not applicable",
# # "retrieved_docs": retrieved_docs_crisis
# }
with st.chat_message("assistant"):
st.write("✅ No immediate crisis detected.")
st.write("🔍 Let's understand your mental health condition...")
condition_result = run_condition_classification(contextual_query, json.dumps(st.session_state.user_profile))
condition = condition_result.get("condition", "general").split(" ")[0].lower()
condition_explanation = condition_result.get("rationale", "")
questionnaire_name = config["CONDITION_TO_QUESTIONNAIRE"].get(condition)
# Store condition detection in session state
st.session_state.detected_condition = condition
st.session_state.condition_explanation = condition_explanation
with st.chat_message("assistant"):
st.success(f"🧠 Based on your message, we identified: **{condition.upper()}**")
if not questionnaire_name:
st.info("No questionnaire available for this condition.")
interpretation = "Not applicable"
else:
st.info(f"We recommend a brief **{questionnaire_name}** assessment to understand your situation better.")
st.session_state.assessment_needed = True
st.session_state.temp_user_query = user_query
st.session_state.temp_condition = condition
st.session_state.questionnaire_name = questionnaire_name
return None
final_rec = run_recommendations(
contextual_query,
json.dumps(st.session_state.user_profile),
condition,
json.dumps({}),
"N/A",
is_crisis="false"
)
# task_outputs = final_rec.tasks_output
# retrieved_docs = task_outputs[0]
with st.chat_message("assistant"):
st.write("💡 Here's your personalized mental health recommendation:")
def stream_answer():
for word in final_rec["recommendation"].split(" "):
yield word + " "
time.sleep(0.1)
st.write_stream(stream_answer)
return final_rec
# return {
# "recommendation": final_rec["recommendation"],
# "score_interpretation": interpretation,
# "condition": condition,
# "is_crisis": False,
# "crisis_explanation": explanation,
# # "retrieved_docs": retrieved_docs
# }
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