Update app.py

app.py

@@ -1,76 +1,217 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
+import os
+import hashlib
+import requests
+from datetime import datetime
+from PIL import Image
+from fpdf import FPDF
+import plotly.graph_objects as go
+import google.generativeai as genai
 import folium
 from streamlit_folium import st_folium
 from streamlit_geolocation import streamlit_geolocation
-import requests
-import json
-
-# --- 1. THE ADVANCED LIVE MAP ENGINE ---
-def render_live_map():
-    st.markdown('<div class="clinical-card"><h3>📍 Live Geo-Medical Intel</h3><p>Detect your location to find real-time hospitals and clinics nearby.</p></div>', unsafe_allow_html=True)
-    
-    # 1. Capture User Coordinates
-    location = streamlit_geolocation()
-    
-    if location and location.get("latitude"):
-        lat = location["latitude"]
-        lon = location["longitude"]
-        
-        st.success(f"Location Detected: {lat:.4f}, {lon:.4f}")
-        
-        # 2. Fetch Live Data from OpenStreetMap (Overpass API)
-        # We search for 'hospital' and 'clinic' within a 5000m radius
-        with st.spinner("Searching for nearby medical facilities..."):
-            overpass_url = "http://overpass-api.de/api/interpreter"
-            overpass_query = f"""
-            [out:json];
-            (
-              node["amenity"="hospital"](around:5000,{lat},{lon});
-              node["amenity"="clinic"](around:5000,{lat},{lon});
-            );
-            out body;
-            """
-            response = requests.get(overpass_url, params={'data': overpass_query})
-            data = response.json()
-        
-        # 3. Process Data & Create Map
-        m = folium.Map(location=[lat, lon], zoom_start=14)
-        
-        # Add User Marker
-        folium.Marker(
-            [lat, lon], 
-            popup="You are here", 
-            icon=folium.Icon(color="blue", icon="user")
-        ).add_to(m)
+from groq import Groq
+
+# --- 1. CORE SYSTEM CONFIG ---
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
+
+if not GROQ_API_KEY or not GOOGLE_API_KEY:
+    st.error("⚠️ API Keys Missing! Please set them in Space Secrets.")
+    st.stop()
+
+genai.configure(api_key=GOOGLE_API_KEY)
+vision_model = genai.GenerativeModel('gemini-2.0-flash')
+
+st.set_page_config(page_title="IntelliCare Portal | Hassan Naseer", layout="wide", page_icon="🏥")
+
+# --- 2. THEME & PDF GENERATOR ---
+if "theme" not in st.session_state: st.session_state.theme = "Light"
+
+def generate_medical_pdf(chat_history, username):
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", 'B', 16)
+    pdf.cell(200, 10, txt="IntelliCare Portal - Clinical Summary", ln=True, align='C')
+    pdf.set_font("Arial", size=10)
+    pdf.cell(200, 10, txt=f"Patient: {username} | Date: {datetime.now().strftime('%Y-%m-%d %H:%M')}", ln=True, align='C')
+    pdf.ln(10)
+    for msg in chat_history:
+        role = "PATIENT" if msg["role"] == "user" else "CLINICAL AI"
+        pdf.set_font("Arial", 'B', 10)
+        pdf.cell(0, 10, txt=f"{role}:", ln=True)
+        pdf.set_font("Arial", size=10)
+        pdf.multi_cell(0, 8, txt=msg["content"])
+        pdf.ln(4)
+    pdf.ln(10)
+    pdf.set_font("Arial", 'I', 8)
+    pdf.multi_cell(0, 5, txt="DISCLAIMER: This is an AI-generated summary for information purposes only. Consult a human professional for definitive diagnosis.")
+    return pdf.output(dest='S').encode('latin-1')
+
+def inject_theme():
+    bg = "#000000" if st.session_state.theme == "Dark" else "#FFFFFF"
+    txt = "#FFFFFF" if st.session_state.theme == "Dark" else "#000000"
+    card = "#111111" if st.session_state.theme == "Dark" else "#F0F2F6"
+    brd = "#FFFFFF" if st.session_state.theme == "Dark" else "#000000"
+    st.markdown(f"""<style>.stApp {{ background-color: {bg} !important; color: {txt} !important; }}
+    div[data-baseweb="input"], div[data-baseweb="textarea"], select {{ border: 2px solid {brd} !important; border-radius: 10px !important; }}
+    .chat-bubble {{ padding: 15px; border-radius: 15px; margin-bottom: 10px; border: 1px solid {brd}; }}
+    .user-msg {{ background-color: rgba(59, 130, 246, 0.1); border-left: 5px solid #3b82f6; }}
+    .ai-msg {{ background-color: rgba(16, 185, 129, 0.1); border-left: 5px solid #10b981; }}
+    .clinical-card {{ background-color: {card}; border: 2px solid {brd}; padding: 25px; border-radius: 15px; color: {txt}; }}
+    [data-testid="stSidebar"] * {{ color: {txt} !important; }}</style>""", unsafe_allow_html=True)
+
+inject_theme()
+
+# --- 3. DATA PERSISTENCE ---
+USER_DB, HISTORY_DB = "users_secure.csv", "clinical_history.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)
+
+def get_vector_db():
+    import chromadb
+    from chromadb.utils import embedding_functions
+    client = chromadb.PersistentClient(path="./med_vector_db")
+    emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="paraphrase-MiniLM-L3-v2")
+    main = client.get_or_create_collection(name="intellicare_vFinal", embedding_function=emb_fn)
+    signal = client.get_or_create_collection(name="signals_targeted", embedding_function=emb_fn)
+    return main, signal
+
+# --- 4. AUTHENTICATION ---
+if "logged_in" not in st.session_state: st.session_state.logged_in = False
+if "last_processed_audio" not in st.session_state: st.session_state.last_processed_audio = None
+
+if not st.session_state.logged_in:
+    st.markdown("<h1 style='text-align: center;'>🏥 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", key="l_u"), st.text_input("Password", type="password", key="l_p")
+            if st.button("Sign In"):
+                users = load_db(USER_DB, ["username", "password", "role"])
+                match = users[(users['username'] == u) & (users['password'] == hash_pass(p))]
+                if not match.empty:
+                    st.session_state.logged_in, st.session_state.username = True, u
+                    st.session_state.role, st.session_state.msgs = match.iloc[0]['role'], []
+                    st.rerun()
+        with tab2:
+            nu, np, nr = st.text_input("New User"), st.text_input("New Pass", type="password"), st.selectbox("Role", ["Patient", "Doctor"])
+            if st.button("Register"):
+                df = load_db(USER_DB, ["username", "password", "role"])
+                if nu not in df['username'].values:
+                    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} ({st.session_state.role})")
+    if st.button("🌓 Toggle Theme"):
+        st.session_state.theme = "Dark" if st.session_state.theme == "Light" else "Light"
+        st.rerun()
+    if st.button("Logout"): st.session_state.logged_in = False; st.rerun()
+    st.divider()
+    nav = st.radio("Menu", ["💬 AI Chat", "🧪 Health Lab", "📍 Nearby Hospitals", "📞 Video Call", "📜 History"]) if st.session_state.role == "Patient" else st.radio("Menu", ["🖥️ Consultation Desk", "📋 Patient Records"])
+
+# --- 6. PATIENT PORTAL (STRICT MEDICAL AI) ---
+if st.session_state.role == "Patient":
+    if nav == "💬 AI Chat":
+        st.markdown('<div class="clinical-card"><h3>💬 Clinical AI Specialist</h3></div>', unsafe_allow_html=True)
+        for m in st.session_state.msgs:
+            bubble = "user-msg" if m["role"] == "user" else "ai-msg"
+            st.markdown(f'<div class="chat-bubble {bubble}"><b>{m["role"].upper()}:</b><br>{m["content"]}</div>', unsafe_allow_html=True)
         
-        hosp_list = []
-        for element in data.get('elements', []):
-            h_lat = element['lat']
-            h_lon = element['lon']
-            name = element.get('tags', {}).get('name', 'Unnamed Medical Facility')
-            hosp_list.append({"Name": name, "Lat": h_lat, "Lon": h_lon})
-            
-            folium.Marker(
-                [h_lat, h_lon], 
-                popup=name, 
-                icon=folium.Icon(color="red", icon="plus-sign")
-            ).add_to(m)
+        q = st.chat_input("Explain symptoms...")
+        c1, c2, c3 = st.columns(3)
+        with c1: v = st.audio_input("🎤 Voice", key=f"v_{len(st.session_state.msgs)}")
+        with c2: up_pdf = st.file_uploader("📄 PDF", type=['pdf'], key=f"p_{len(st.session_state.msgs)}")
+        with c3: up_img = st.file_uploader("📸 Scan", type=['png', 'jpg'], key=f"i_{len(st.session_state.msgs)}")
         
-        # 4. Display Map and Data Table
-        st_folium(m, width=1200, height=500)
+        final_q = q if q else None
+        if up_pdf:
+            import pdfplumber
+            with pdfplumber.open(up_pdf) as f: final_q = "Analyze these medical records: " + " ".join([p.extract_text() for p in f.pages if p.extract_text()])
+        elif up_img:
+            res = vision_model.generate_content(["Extract clinical data from this medical image:", Image.open(up_img)])
+            final_q = "Medical Image Data: " + res.text
+        elif v:
+            v_hash = hashlib.md5(v.getvalue()).hexdigest()
+            if v_hash != st.session_state.last_processed_audio:
+                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_processed_audio = v_hash
+
+        if final_q:
+            st.session_state.msgs.append({"role": "user", "content": final_q})
+            # --- STRICT MEDICAL GUARDRAIL ---
+            sys_prompt = "You are a specialized Clinical AI. Only answer medical, health, and pharmacological questions. Politely decline non-medical topics. Be precise and ground answers in provided context."
+            main_col, _ = get_vector_db()
+            res = main_col.query(query_texts=[final_q], n_results=1)
+            ctx = res['documents'][0][0] if (res.get('documents') and len(res['documents'][0]) > 0) else "N/A"
+            ans = Groq(api_key=GROQ_API_KEY).chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": sys_prompt}, {"role": "system", "content": f"Context: {ctx}"}] + st.session_state.msgs)
+            st.session_state.msgs.append({"role": "assistant", "content": ans.choices[0].message.content})
+            st.rerun()
+
+        if st.session_state.msgs:
+            if st.download_button("📥 Download Summary (PDF)", generate_medical_pdf(st.session_state.msgs, st.session_state.username), f"IntelliCare_Summary_{st.session_state.username}.pdf", "application/pdf"):
+                st.success("Summary Generated!")
+
+    elif nav == "🧪 Health Lab":
+        st.markdown('<div class="clinical-card"><h3>🧪 Clinical Diagnostics</h3></div>', unsafe_allow_html=True)
+        col1, col2 = st.columns(2)
+        with col1:
+            w, h_cm = st.number_input("Weight (kg)", 30, 200, 70), st.number_input("Height (cm)", 100, 250, 175)
+            bmi = round(w / ((h_cm/100)**2), 1)
+            fig_bmi = go.Figure(go.Indicator(mode="gauge+number", value=bmi, domain={'x': [0, 1], 'y': [0, 1]}, gauge={'axis': {'range': [10, 40]}, 'bar': {'color': "#10b981"}, 'steps': [{'range': [10, 18.5], 'color': "lightblue"}, {'range': [25, 40], 'color': "orange"}]}))
+            st.plotly_chart(fig_bmi, use_container_width=True)
+        with col2:
+            hr = st.slider("Heart Rate (BPM)", 40, 180, 72)
+            t = np.linspace(0, 2, 200)
+            y = np.sin(2 * np.pi * (hr/60) * t) + 0.5 * np.sin(4 * np.pi * (hr/60) * t)
+            st.plotly_chart(go.Figure(data=go.Scatter(y=y, mode='lines', line=dict(color='#ff4b4b', width=3))), use_container_width=True)
         
-        if hosp_list:
-            st.markdown("#### 📋 Found Facilities")
-            st.dataframe(pd.DataFrame(hosp_list))
-        else:
-            st.info("No hospitals found within 5km of your location.")
-            
-    else:
-        st.info("Please click the button above to allow location access.")
-
-# --- 2. INTEGRATION INTO YOUR APP ---
-# Replace your previous "📍 Nearby Hospitals" logic with this:
-if nav == "📍 Nearby Hospitals":
-    render_live_map()
+
+    elif nav == "📍 Nearby Hospitals":
+        loc = streamlit_geolocation()
+        if loc and loc.get("latitude"):
+            lat, lon = loc["latitude"], loc["longitude"]
+            query = f'[out:json];node["amenity"~"hospital|clinic"](around:5000,{lat},{lon});out body;'
+            data = requests.get("http://overpass-api.de/api/interpreter", params={'data': query}).json()
+            m = folium.Map(location=[lat, lon], zoom_start=14)
+            folium.Marker([lat, lon], popup="You", icon=folium.Icon(color="blue")).add_to(m)
+            for e in data.get('elements', []): folium.Marker([e['lat'], e['lon']], popup=e.get('tags', {}).get('name', 'Clinic'), icon=folium.Icon(color="red")).add_to(m)
+            st_folium(m, width=1200, height=500)
+        else: st.info("Enable location to scan local clinics.")
+
+    elif nav == "📞 Video Call":
+        docs = load_db(USER_DB, ["username", "role"])
+        sel = st.selectbox("Select Doctor", docs[docs['role'] == 'Doctor']['username'].tolist())
+        if st.button("Request Connection"):
+            room = f"IntelliCare-{st.session_state.username}-{sel}"
+            _, signal_col = get_vector_db()
+            signal_col.upsert(documents=[f"CALL|{sel}|{room}|{st.session_state.username}"], ids=["latest"])
+            st.session_state.active_room = room
+        if "active_room" in st.session_state:
+            st.components.v1.html(f'<iframe src="https://meet.jit.si/{st.session_state.active_room}" width="100%" height="550px"></iframe>', height=600)
+
+# --- 7. DOCTOR PORTAL ---
+elif st.session_state.role == "Doctor":
+    if nav == "🖥️ Consultation Desk":
+        st.markdown('<div class="clinical-card"><h3>🖥️ Consultation Desk</h3></div>', unsafe_allow_html=True)
+        _, signal_col = get_vector_db()
+        res = signal_col.get(ids=["latest"])
+        if res.get('documents'):
+            parts = res['documents'][0].split("|") # CALL|DocName|Room|Patient
+            if parts[1] == st.session_state.username:
+                st.warning(f"🔔 {parts[3]} is requesting a call.")
+                if st.button("✅ Join Call"): st.session_state.active_call = parts[2]
+                if "active_call" in st.session_state:
+                    st.components.v1.html(f'<iframe src="https://meet.jit.si/{parts[2]}" width="100%" height="600px"></iframe>', height=650)
+                    if st.button("🔴 End & Archive"):
+                        df = load_db(HISTORY_DB, ["Time", "Patient", "Doctor", "Status"])
+                        pd.concat([df, pd.DataFrame([{"Time": datetime.now().strftime("%Y-%m-%d %H:%M"), "Patient": parts[3], "Doctor": st.session_state.username, "Status": "Completed"}])]).to_csv(HISTORY_DB, index=False)
+                        signal_col.delete(ids=["latest"]); del st.session_state.active_call; st.rerun()