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from vocca_ai.ai_response import generate_call_summary
from vocca_ai.intent_classifier import classify_intent
from vocca_ai.sentiment import analyze_sentiment
from vocca_ai.db_handler import log_call, fetch_recent_calls
import streamlit as st
from vocca_ai.preprocess import priority_score
from vocca_ai.intent_classifier import classify_intent
import sys
import os
# this line ensures Python can find the 'models' directory
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
st.title("🩺 AI-Powered Call Insights for Vocca")
st.write("Analyze patient calls, detect urgency, and generate AI-powered responses.")
user_input = st.text_area("📞 Enter Call Transcript:", height=250)
if user_input:
intent = classify_intent(user_input)
priority = priority_score(user_input)
sentiment = analyze_sentiment(user_input) # Now using DistilBERT
ai_response = generate_call_summary(user_input) # Now using Falcon-7B
st.subheader(" Extracted Call Insights")
st.write(f"**Intent:** {intent}")
st.write(f"**Priority Level:** {priority}")
st.write(f"**Sentiment:** {sentiment}")
st.write(f"**AI Suggested Response:** {ai_response}")
log_call(user_input, intent, priority, sentiment, ai_response)
st.success("✅ Call successfully logged & analyzed!")
if st.button("📊 Show Recent Calls"):
calls = fetch_recent_calls()
st.subheader("📊 Recent Call Logs")
for row in calls:
st.write(f" **Transcript:** {row[1]}")
st.write(f" **Intent:** {row[2]}, **Priority:** {row[3]}, **Sentiment:** {row[4]}")
st.write(f" **AI Response:** {row[5]}")
st.write("---")
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