app.py

import streamlit as st
import plotly.graph_objects as go
from faster_whisper import WhisperModel
import requests
import json
import time
import io
from audio_recorder_streamlit import audio_recorder
from PIL import Image

# --- 1. CONFIGURATION ---
st.set_page_config(page_title="SomAI", layout="wide", page_icon="🩺")
BACKEND_API_URL = "https://arshenoy/somAI-backend.hf.space" 

@st.cache_resource
def load_whisper():
    print(">>> LOADING AUDIO SENSORS...")
    whisper_model = WhisperModel("tiny", device="cpu", compute_type="int8") 
    return whisper_model

try:
    whisper = load_whisper()
except Exception as e:
    st.error(f"WHISPER FAILURE: {e}")
    st.stop()

# --- 3. NEW NEON STYLE CSS ---
st.markdown("""
<style>
    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600&family=JetBrains+Mono:wght@400;700&display=swap');

    /* BASE THEME - Deep Space Black */
    .stApp {
        background-color: #050505;
        background-image: radial-gradient(circle at 50% 50%, #1a1a1a 0%, #000000 100%);
        font-family: 'Inter', sans-serif;
    }

    /* GLASS SIDEBAR */
    section[data-testid="stSidebar"] {
        background: rgba(10, 10, 10, 0.7);
        backdrop-filter: blur(12px);
        border-right: 1px solid rgba(255, 255, 255, 0.08);
    }

    /* NEON METRICS */
    div[data-testid="metric-container"] {
        background: rgba(255, 255, 255, 0.03);
        border: 1px solid rgba(255, 255, 255, 0.1);
        border-radius: 12px;
        padding: 15px;
        transition: 0.3s;
    }
    div[data-testid="metric-container"]:hover {
        border-color: #00ff80;
        box-shadow: 0 0 15px rgba(0, 255, 128, 0.1);
    }

    /* TEXT GLOW */
    h1, h2, h3 {
        font-family: 'JetBrains Mono', monospace;
        letter-spacing: -0.5px;
        color: #fff;
        text-shadow: 0 0 10px rgba(255, 255, 255, 0.2);
    }
    
    /* CHAT BUBBLES - Updated for new dark background */
    .chat-bubble {
        padding: 12px 16px;
        border-radius: 8px;
        margin-bottom: 10px;
        line-height: 1.5;
        font-family: 'Inter', sans-serif; 
        font-size: 16px;
        color: #e0e0e0;
    }
    .user-bubble {
        background-color: #004d26; /* Darker green for user */
        margin-left: 20%; 
        border-radius: 12px 12px 0 12px; 
        text-align: right;
    }
    .ai-bubble {
        background-color: #1a1a1a; /* Dark gray for AI */
        border: 1px solid rgba(255, 255, 255, 0.1);
        margin-right: 20%; 
        border-radius: 12px 12px 12px 0; 
        text-align: left;
    }

    /* REMOVE JUNK */
    #MainMenu {visibility: hidden;}
    footer {visibility: hidden;}
</style>
""", unsafe_allow_html=True)


# --- 4. SESSION STATE ---
if 'history' not in st.session_state: st.session_state.history = []
if 'risk_score' not in st.session_state: st.session_state.risk_score = 0
if 'risk_summary' not in st.session_state: st.session_state.risk_summary = "Pending Analysis"
if 'mode' not in st.session_state: st.session_state.mode = "GENERAL"

# --- 5. SIDEBAR ---
with st.sidebar:
    st.markdown("### 🧬 Patient Intake & Vitals")
    with st.expander("Patient Profile", expanded=True):
        name = st.text_input("Name", "Patient X")
        age = st.slider("Age", 18, 90, 20)
        condition = st.text_input("Primary Condition", "Diabetes/Hypertension")
                
    with st.expander("Clinical Vitals", expanded=True):
        bp = st.number_input("Systolic BP", 90, 220, 110)
        glucose = st.number_input("Glucose", 70, 400, 110)
        sleep = st.slider("Sleep Quality (0-10)", 0, 10, 4)
        meds = st.slider("Missed Doses (Last 7 Days)", 0, 7, 3)

    if st.button("RUN CLINICAL ANALYSIS", type="primary", use_container_width=True):
        with st.spinner("Analyzing Clinical Markers..."):
            try:
                # API CALL TO /analyze ENDPOINT
                payload = {
                    "age": age,
                    "condition": condition,
                    "sleep_quality": sleep,
                    "missed_doses": meds,
                    "systolic_bp": bp,
                    "glucose": glucose
                }
                response = requests.post(f"{BACKEND_API_URL}/analyze", json=payload, timeout=30)
                response.raise_for_status() # Raises an HTTPError for bad responses (4xx or 5xx)
                
                data = response.json()
                st.session_state.risk_score = data['numeric_score']
                st.session_state.risk_summary = data['risk_summary']
            
            except requests.exceptions.RequestException as req_err:
                st.error(f"API Error: Cannot connect to backend (Code: {req_err.response.status_code if hasattr(req_err, 'response') and req_err.response else 'N/A'}). Ensure Space 2 is running.")
                st.session_state.risk_score = 0
                st.session_state.risk_summary = "Backend service unavailable."
            except Exception as e:
                st.error(f"Analysis Failed: {e}")
                st.session_state.risk_score = 0
                st.session_state.risk_summary = "Processing error."


    val = st.session_state.risk_score
    color = "#00ff80" if val < 40 else "#ffc300" if val < 80 else "#ff3300" # Neon color scheme

    # Gauge Chart
    fig = go.Figure(go.Indicator(
        mode="gauge+number", 
        value=val, 
        number={'font': {'size': 40, 'color': 'white'}}, 
        gauge={
            'axis': {'range': [0, 100], 'tickcolor': '#333333'}, 
            'bar': {'color': color}, 
            'bgcolor': "rgba(26, 26, 26, 0.7)", 
            'bordercolor': "#333333",
            'steps': [
                {'range': [0, 40], 'color': 'rgba(0, 255, 128, 0.1)'},
                {'range': [40, 80], 'color': 'rgba(255, 195, 0, 0.1)'},
                {'range': [80, 100], 'color': 'rgba(255, 51, 0, 0.1)'},
            ]
        }
    ))
    fig.update_layout(
        height=250, 
        margin=dict(l=10,r=10,t=30,b=10), 
        paper_bgcolor="rgba(0,0,0,0)", 
        font={'color': 'white', 'family': 'JetBrains Mono'}
    )
    st.plotly_chart(fig, use_container_width=True)
    
    st.markdown(f"""
    <div style="background: rgba(255, 255, 255, 0.05); border: 1px solid rgba(255, 255, 255, 0.1); border-radius: 12px; padding: 15px; margin-top: 15px; border-left: 4px solid {color};">
        <h5 style="margin:0; color: {color}; font-family: 'JetBrains Mono', monospace;">CLINICAL ASSESSMENT</h5>
        <p style="margin-top:5px; font-size: 0.9rem; color: #ddd;">{st.session_state.risk_summary}</p>
    </div>
    """, unsafe_allow_html=True)

# --- 6. MAIN CHAT ---
col_main = st.columns(1)[0] 
with col_main:
    st.markdown("## 🧠 SomAI Assistant")
    c1, c2 = st.columns(2)
    if c1.button("🩺 Medical Guide", use_container_width=True, type="primary" if st.session_state.mode == "GENERAL" else "secondary"): st.session_state.mode = "GENERAL"
    if c2.button("🫂 Therapist", use_container_width=True, type="primary" if st.session_state.mode == "THERAPY" else "secondary"): st.session_state.mode = "THERAPY"
    
    # Display History
    chat_container = st.container(height=400)
    for msg in st.session_state.history:
        div_class = "user-bubble" if msg['role'] == "user" else "ai-bubble"
        chat_container.markdown(f"<div class='chat-bubble {div_class}'>{msg['content']}</div>", unsafe_allow_html=True)
    
    # --- ROBUST AUDIO INPUT (Hold and Speak) ---
    st.markdown("---")
    st.markdown("🎙️ **Hold & Speak:**")
    audio_bytes = audio_recorder(
        text="",
        recording_color="#ff3300", 
        neutral_color="#00ff80",   
        icon_name="microphone",
        icon_size="3x",
        initial_time=0 
    )
    
    user_query = None
    
    # 1. VOICE PROCESSING
    if audio_bytes:
        with st.spinner("🔊 Transcribing Voice..."):
            audio_file = io.BytesIO(audio_bytes)
            segments, info = whisper.transcribe(audio_file, beam_size=5) 
            text_list = [segment.text for segment in segments]
            user_query = " ".join(text_list).strip()
            if not user_query:
                st.warning("Could not detect speech. Please speak clearly.")
                st.stop()
            st.session_state.history.append({"role": "user", "content": user_query})
            chat_container.markdown(f"<div class='chat-bubble user-bubble'>{user_query}</div>", unsafe_allow_html=True)
    
    # 2. TEXT PROCESSING
    text_input = st.chat_input("...or type a message")
    if text_input:
        user_query = text_input
        st.session_state.history.append({"role": "user", "content": user_query})
        chat_container.markdown(f"<div class='chat-bubble user-bubble'>{user_query}</div>", unsafe_allow_html=True)
    
    # 3. QUERY LOGIC (API Call)
    if user_query:
        
        # --- LLM Response Generation (Streaming Emulation) ---
        placeholder = chat_container.empty()
        full_resp = ""
        
        with placeholder.container():
            with st.spinner("Thinking..."):
                try:
                    # API Call to /generate ENDPOINT
                    payload = {
                        "query": user_query,
                        "age": age,
                        "condition": condition,
                        "mode": st.session_state.mode
                    }
                    

                    response = requests.post(f"{BACKEND_API_URL}/generate", json=payload, timeout=60)
                    response.raise_for_status() 
                    
                    data = response.json()
                    raw_text = data['generated_text']
                    

                    chunk_size = 5
                    words = raw_text.split()
                    
                    for i in range(0, len(words), chunk_size):
                        chunk = " ".join(words[i:i + chunk_size])
                        full_resp += chunk + " "
                        

                        placeholder.markdown(f"<div class='chat-bubble ai-bubble'>{full_resp}▌</div>", unsafe_allow_html=True)
                        time.sleep(0.05) # Adjust for speed
                    

                    placeholder.markdown(f"<div class='chat-bubble ai-bubble'>{raw_text}</div>", unsafe_allow_html=True)
                    
                    st.session_state.history.append({"role": "assistant", "content": raw_text})
                    
                    suggestions = data.get('suggestions', [])
                    if suggestions:
                        st.markdown("---")
                        st.markdown("💡 **Next Steps:**")
                        suggestion_cols = st.columns(len(suggestions))
                        for i, sug in enumerate(suggestions):
                            suggestion_cols[i].button(sug, key=f"sug_{i}_{len(st.session_state.history)}", use_container_width=True)

                except requests.exceptions.RequestException as req_err:
                    error_msg = f"API Error: {req_err}. Check backend service health."
                    st.error(error_msg)
                    st.session_state.history.append({"role": "assistant", "content": error_msg})
                    placeholder.markdown(f"<div class='chat-bubble ai-bubble'>{error_msg}</div>", unsafe_allow_html=True)
                except Exception as e:
                    error_msg = f"LLM Generation Failed: {e}"
                    st.error(error_msg)
                    st.session_state.history.append({"role": "assistant", "content": error_msg})
                    placeholder.markdown(f"<div class='chat-bubble ai-bubble'>{error_msg}</div>", unsafe_allow_html=True)
        

        st.rerun()