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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import os | |
| import hashlib | |
| from datetime import datetime | |
| from groq import Groq | |
| import pdfplumber | |
| import plotly.graph_objects as go | |
| import folium | |
| from streamlit_folium import st_folium | |
| from streamlit_geolocation import streamlit_geolocation | |
| # --- 1. CORE SYSTEM CONFIG --- | |
| GROQ_API_KEY = os.environ.get("GROQ_API_KEY") | |
| if not GROQ_API_KEY: | |
| st.error("⚠️ API Key Missing! Please check your environment variables.") | |
| st.stop() | |
| st.set_page_config(page_title="IntelliCare Portal | Hassan Naseer", layout="wide", page_icon="🏥") | |
| # --- 2. PREMIUM CSS --- | |
| st.markdown(""" | |
| <style> | |
| [data-testid="stSidebar"] { background: #1e3a8a !important; } | |
| [data-testid="stSidebar"] * { color: #ffffff !important; } | |
| div.stButton > button { | |
| background-color: #1e3a8a !important; color: white !important; | |
| border: 1px solid #3b82f6 !important; transition: none !important; | |
| } | |
| .user-msg { | |
| background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%); | |
| color: white; padding: 15px; border-radius: 18px 18px 2px 18px; | |
| margin-bottom: 15px; margin-left: 20%; box-shadow: 0 4px 10px rgba(37,99,235,0.2); | |
| } | |
| .ai-msg { | |
| background: #f1f5f9; color: #1e293b; padding: 15px; border-radius: 18px 18px 18px 2px; | |
| margin-bottom: 15px; margin-right: 20%; border: 1px solid #e2e8f0; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # --- 3. DATA UTILS --- | |
| USER_DB, CALL_LOG_DB, CALL_SIGNAL_DB = "users_secure.csv", "call_history.csv", "active_calls.csv" | |
| def hash_pass(pwd): return hashlib.sha256(str.encode(pwd)).hexdigest() | |
| def load_db(file, cols): | |
| if os.path.exists(file): return pd.read_csv(file) | |
| return pd.DataFrame(columns=cols) | |
| # Session State Initialization | |
| if "logged_in" not in st.session_state: st.session_state.logged_in = False | |
| if "msgs" not in st.session_state: st.session_state.msgs = [] | |
| if "active_doc" not in st.session_state: st.session_state.active_doc = "" | |
| if "last_voice_hash" not in st.session_state: st.session_state.last_voice_hash = None | |
| if "audio_key" not in st.session_state: st.session_state.audio_key = 0 # To reset Mic | |
| # --- 4. AUTHENTICATION --- | |
| if not st.session_state.logged_in: | |
| st.markdown("<h1 style='text-align: center; color: #1e3a8a;'>🏥 IntelliCare Portal</h1>", unsafe_allow_html=True) | |
| c2 = st.columns([1, 2, 1])[1] | |
| with c2: | |
| tab1, tab2 = st.tabs(["🔐 Login", "📝 Create Account"]) | |
| with tab1: | |
| u, p = st.text_input("Username"), st.text_input("Password", type="password") | |
| if st.button("Sign In"): | |
| db = load_db(USER_DB, ["username", "password", "role"]) | |
| match = db[(db['username'] == u) & (db['password'] == hash_pass(p))] | |
| if not match.empty: | |
| st.session_state.logged_in, st.session_state.username = True, u | |
| st.session_state.role = match.iloc[0]['role'] | |
| st.rerun() | |
| with tab2: | |
| nu, np = st.text_input("New ID"), st.text_input("New Pass", type="password") | |
| nr = st.selectbox("Role", ["Patient", "Doctor"]) | |
| if st.button("Register"): | |
| df = load_db(USER_DB, ["username", "password", "role"]) | |
| pd.concat([df, pd.DataFrame([{"username": nu, "password": hash_pass(np), "role": nr}])]).to_csv(USER_DB, index=False) | |
| st.success("Registered!") | |
| st.stop() | |
| # --- 5. SIDEBAR NAVIGATION --- | |
| with st.sidebar: | |
| st.markdown(f"### 👤 {st.session_state.username}") | |
| lang = st.radio("🌐 Language", ["English", "Urdu"]) | |
| # PERMANENT DOWNLOAD | |
| chat_txt = "\n".join([f"{m['role']}: {m['content']}" for m in st.session_state.msgs]) | |
| st.download_button("📥 Download History", data=chat_txt, file_name="chat.txt") | |
| if st.button("🗑️ Clear Chat"): | |
| st.session_state.msgs, st.session_state.active_doc = [], "" | |
| st.rerun() | |
| if st.button("🚪 Logout"): st.session_state.logged_in = False; st.rerun() | |
| st.divider() | |
| if st.session_state.role == "Patient": | |
| nav = st.radio("Menu", ["💬 AI Chat", "🧪 Health Lab", "📍 Nearby Clinics", "📞 Video Consult", "📜 History"]) | |
| else: | |
| nav = st.radio("Menu", ["🖥️ Consultation Desk", "📜 Call Logs"]) | |
| # --- 6. AI CHAT MODULE (FIXED MIC & INPUT) --- | |
| if nav == "💬 AI Chat": | |
| st.markdown("### 💬 Clinical Intelligence Assistant") | |
| with st.expander("📄 Upload Report/Prescription"): | |
| up = st.file_uploader("Drop PDF", type=['pdf'], label_visibility="collapsed") | |
| if up: | |
| with pdfplumber.open(up) as f: | |
| st.session_state.active_doc = " ".join([p.extract_text() for p in f.pages if p.extract_text()]) | |
| st.success("✅ Analysis Ready!") | |
| for m in st.session_state.msgs: | |
| st.markdown(f'<div class="{"user-msg" if m["role"]=="user" else "ai-msg"}">{m["content"]}</div>', unsafe_allow_html=True) | |
| st.divider() | |
| col_v, col_q = st.columns([1, 9]) | |
| with col_v: # FIXED MIC RESET | |
| v = st.audio_input("🎤", key=f"mic_{st.session_state.audio_key}", label_visibility="collapsed") | |
| with col_q: q = st.chat_input("Ask about your health...") | |
| final_q = q if q else None | |
| if v and not final_q: | |
| vh = hashlib.md5(v.getvalue()).hexdigest() | |
| if vh != st.session_state.last_voice_hash: | |
| final_q = Groq(api_key=GROQ_API_KEY).audio.transcriptions.create(file=("a.wav", v.getvalue()), model="whisper-large-v3", response_format="text") | |
| st.session_state.last_voice_hash = vh | |
| st.session_state.audio_key += 1 # Forces Mic refresh | |
| if final_q: | |
| st.session_state.msgs.append({"role": "user", "content": final_q}) | |
| sys_p = f"Professional Medical Assistant. Language: {lang}. Analyze the PDF text provided. Refuse non-medical tasks." | |
| ans = Groq(api_key=GROQ_API_KEY).chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": sys_p}, {"role": "system", "content": f"PDF: {st.session_state.active_doc}"}] + st.session_state.msgs) | |
| st.session_state.msgs.append({"role": "assistant", "content": ans.choices[0].message.content}) | |
| st.rerun() | |
| # --- 7. DIAGNOSTIC TOOLS (3 TOOLS RESTORED) --- | |
| elif nav == "🧪 Health Lab": | |
| st.markdown("### 🧪 Interactive Clinical Lab") | |
| tool = st.selectbox("Select Diagnostic Tool", ["⚖️ BMI Analyzer", "🩸 Glucose Tracker", "🫀 Heart Rate Sim"]) | |
| if tool == "⚖️ BMI Analyzer": | |
| w, h = st.number_input("Weight (kg)", 30, 200, 70), st.number_input("Height (cm)", 100, 250, 175) | |
| bmi = round(w / ((h/100)**2), 1) | |
| st.metric("Your BMI", bmi) | |
| st.plotly_chart(go.Figure(go.Indicator(mode="gauge+number", value=bmi, gauge={'axis': {'range': [10, 40]}, 'bar': {'color': "#3b82f6"}})), use_container_width=True) | |
| elif tool == "🩸 Glucose Tracker": | |
| st.write("Recent Blood Sugar Levels (mg/dL)") | |
| df = pd.DataFrame({'Time': range(1, 11), 'Level': np.random.randint(80, 150, 10)}) | |
| st.plotly_chart(go.Figure(data=go.Scatter(x=df['Time'], y=df['Level'], mode='lines+markers', line=dict(color='#2563eb'))), use_container_width=True) | |
| elif tool == "🫀 Heart Rate Sim": | |
| bpm = st.slider("Select Current BPM", 40, 180, 72) | |
| st.metric("Heart Rate", f"{bpm} BPM") | |
| t = np.linspace(0, 2, 1000) | |
| heart_wave = np.sin(2 * np.pi * (bpm/60) * t) + 0.1 * np.random.normal(0, 1, 1000) | |
| st.plotly_chart(go.Figure(data=go.Scatter(x=t, y=heart_wave, line=dict(color='#ef4444'))), use_container_width=True) | |
| # --- 8. MAPS & VIDEO CALLS (RESTORED) --- | |
| elif nav == "📍 Nearby Clinics": | |
| st.markdown("### 📍 Specialist Hospital Locator") | |
| loc = streamlit_geolocation() | |
| if loc.get("latitude"): | |
| m = folium.Map(location=[loc["latitude"], loc["longitude"]], zoom_start=14) | |
| folium.Marker([loc["latitude"], loc["longitude"]], popup="You").add_to(m) | |
| st_folium(m, width=1000, height=500) | |
| elif nav == "📞 Video Consult": | |
| db = load_db(USER_DB, ["username", "role"]) | |
| docs = db[db['role'] == 'Doctor']['username'].tolist() | |
| sel_doc = st.selectbox("Select Specialist", docs) | |
| if st.button("Request Video Consultation"): | |
| room = f"IntelliCare-{st.session_state.username}-{sel_doc}" | |
| pd.DataFrame([{"Caller": st.session_state.username, "Receiver": sel_doc, "RoomID": room, "Status": "Active"}]).to_csv(CALL_SIGNAL_DB, index=False) | |
| pd.concat([load_db(CALL_LOG_DB, ["Time", "Caller", "Receiver", "RoomID"]), pd.DataFrame([{"Time": datetime.now().strftime("%Y-%m-%d %H:%M"), "Caller": st.session_state.username, "Receiver": sel_doc, "RoomID": room}])]).to_csv(CALL_LOG_DB, index=False) | |
| st.session_state.p_room = room | |
| if "p_room" in st.session_state: | |
| st.components.v1.html(f'<iframe src="https://meet.jit.si/{st.session_state.p_room}" width="100%" height="600px"></iframe>', height=650) | |
| elif nav == "🖥️ Consultation Desk": | |
| signals = load_db(CALL_SIGNAL_DB, ["Caller", "Receiver", "RoomID"]) | |
| my_calls = signals[signals['Receiver'] == st.session_state.username] | |
| if not my_calls.empty: | |
| st.info(f"🔔 Request from {my_calls.iloc[0]['Caller']}") | |
| c1, c2 = st.columns(2) | |
| with c1: | |
| if st.button("✅ Accept"): st.session_state.d_room = my_calls.iloc[0]['RoomID'] | |
| with c2: | |
| if st.button("❌ Decline"): | |
| load_db(CALL_SIGNAL_DB, ["Caller", "Receiver", "RoomID", "Status"])[load_db(CALL_SIGNAL_DB, ["Caller", "Receiver", "RoomID", "Status"])['Receiver'] != st.session_state.username].to_csv(CALL_SIGNAL_DB, index=False) | |
| st.rerun() | |
| if "d_room" in st.session_state: | |
| st.components.v1.html(f'<iframe src="https://meet.jit.si/{st.session_state.d_room}" width="100%" height="600px"></iframe>', height=650) | |
| elif nav in ["📜 History", "📜 Call Logs"]: | |
| st.dataframe(load_db(CALL_LOG_DB, ["Time", "Caller", "Receiver", "RoomID"]), use_container_width=True) |