Update app.py

app.py

@@ -6,10 +6,6 @@ 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 CONFIGURATION ---
 GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
@@ -19,36 +15,27 @@ if not GROQ_API_KEY:
 
 st.set_page_config(page_title="IntelliCare Portal | Hassan Naseer", layout="wide", page_icon="🏥")
 
-# --- 2. BEAUTIFUL CHAT BUBBLE CSS (Simple Layout, Premium Chat) ---
+# --- 2. PREMIUM CHAT BUBBLE CSS ---
 st.markdown("""
 <style>
-    /* User Message: Beautiful Blue Gradient */
+    /* Beautiful Blue Gradient for Patient */
     .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);
+        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 Message: Professional Soft Background */
+    /* Soft Professional Grey for AI Assistant */
     .ai-msg {
-        background: #f1f5f9;
-        color: #1e293b;
-        padding: 15px;
-        border-radius: 18px 18px 18px 2px;
-        margin-bottom: 15px;
-        margin-right: 20%;
-        border: 1px solid #e2e8f0;
-        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+        background: #f1f5f9; color: #1e293b; padding: 15px; border-radius: 18px 18px 18px 2px;
+        margin-bottom: 15px; margin-right: 20%; border: 1px solid #e2e8f0; box-shadow: 0 2px 5px rgba(0,0,0,0.05);
     }
 </style>
 """, unsafe_allow_html=True)
 
-# --- 3. DATA PERSISTENCE & UTILS ---
+# --- 3. DATA PERSISTENCE & CALL LOGIC ---
 USER_DB = "users_secure.csv"
-HISTORY_DB = "clinical_history.csv"
+CALL_LOG_DB = "call_history.csv" # Tracks caller, receiver, and room
+CALL_SIGNAL_DB = "active_calls.csv" # Real-time signaling for doctors
 
 def hash_pass(pwd): return hashlib.sha256(str.encode(pwd)).hexdigest()
 
@@ -56,10 +43,16 @@ def load_db(file, cols):
     if os.path.exists(file): return pd.read_csv(file)
     return pd.DataFrame(columns=cols)
 
-def save_history(user, text, role):
-    df = load_db(HISTORY_DB, ["Time", "User", "Message", "Role"])
-    new_entry = pd.DataFrame([{"Time": datetime.now().strftime("%Y-%m-%d %H:%M"), "User": user, "Message": text, "Role": role}])
-    pd.concat([df, new_entry]).to_csv(HISTORY_DB, index=False)
+def log_call(caller, receiver, room):
+    df = load_db(CALL_LOG_DB, ["Time", "Caller", "Receiver", "RoomID"])
+    new_call = pd.DataFrame([{"Time": datetime.now().strftime("%Y-%m-%d %H:%M"), "Caller": caller, "Receiver": receiver, "RoomID": room}])
+    pd.concat([df, new_call]).to_csv(CALL_LOG_DB, index=False)
+
+def send_call_request(caller, receiver, room):
+    df = load_db(CALL_SIGNAL_DB, ["Caller", "Receiver", "RoomID", "Status"])
+    df = df[df['Receiver'] != receiver] # Clear previous request for this doctor
+    new_req = pd.DataFrame([{"Caller": caller, "Receiver": receiver, "RoomID": room, "Status": "Active"}])
+    pd.concat([df, new_req]).to_csv(CALL_SIGNAL_DB, index=False)
 
 # Session State Initialization
 if "logged_in" not in st.session_state: st.session_state.logged_in = False
@@ -67,10 +60,10 @@ if "msgs" not in st.session_state: st.session_state.msgs = []
 if "active_doc" not in st.session_state: st.session_state.active_doc = None
 if "last_voice_hash" not in st.session_state: st.session_state.last_voice_hash = None
 
-# --- 4. AUTHENTICATION (Account Creation & Login) ---
+# --- 4. AUTHENTICATION PORTAL ---
 if not st.session_state.logged_in:
     st.markdown("<h1 style='text-align: center;'>🏥 IntelliCare Portal</h1>", unsafe_allow_html=True)
-    c1, c2, c3 = st.columns([1, 2, 1])
+    c2 = st.columns([1, 2, 1])[1]
     with c2:
         tab1, tab2 = st.tabs(["🔐 Login", "📝 Create Account"])
         with tab1:
@@ -82,16 +75,13 @@ if not st.session_state.logged_in:
                     st.session_state.logged_in, st.session_state.username = True, u
                     st.session_state.role = match.iloc[0]['role']
                     st.rerun()
-                else:
-                    st.error("Invalid credentials.")
         with tab2:
             nu, np, nr = st.text_input("New ID"), st.text_input("New Pass", type="password"), st.selectbox("Role", ["Patient", "Doctor"])
             if st.button("Register Account"):
                 df = load_db(USER_DB, ["username", "password", "role"])
-                if nu in df['username'].values: st.warning("User already exists.")
-                else:
+                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("Account Registered! Please Login.") #
+                    st.success("Account created! Please log in.") #
     st.stop()
 
 # --- 5. SIDEBAR NAVIGATION ---
@@ -100,93 +90,76 @@ with st.sidebar:
     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", "📜 My History"])
+        nav = st.radio("Menu", ["💬 AI Chat", "📞 Video Consult", "🧪 Health Lab", "📜 Call History"])
     else:
-        nav = st.radio("Menu", ["🖥️ Consultation Desk", "📋 Patient Archive", "📜 Session Logs"])
+        nav = st.radio("Menu", ["🖥️ Consultation Desk", "📜 Call Logs"])
 
-# --- 6. AI CHAT MODULE (BEAUTIFUL BACKGROUNDS & LEFT ICONS) ---
+# --- 6. PATIENT FEATURES ---
 if nav == "💬 AI Chat":
     st.markdown("### 💬 Clinical AI Assistant")
-    
-    # Render Beautiful Chat
     for m in st.session_state.msgs:
-        div_class = "user-msg" if m["role"] == "user" else "ai-msg"
-        st.markdown(f'<div class="{div_class}">{m["content"]}</div>', unsafe_allow_html=True)
-
+        st.markdown(f'<div class="{"user-msg" if m["role"] == "user" else "ai-msg"}">{m["content"]}</div>', unsafe_allow_html=True)
+    
     st.divider()
-    # MIC AND PLUS ON LEFT OF PROMPT
-    col_v, col_up, col_q = st.columns([0.6, 0.6, 8.8])
-    with col_v: 
-        v = st.audio_input("🎤", label_visibility="collapsed", key=f"mic_{len(st.session_state.msgs)}")
-    with col_up:
+    # MIC AND PLUS ON LEFT OF TEXT INPUT
+    cv, cp, cq = st.columns([0.6, 0.6, 8.8])
+    with cv: v = st.audio_input("🎤", key=f"v_{len(st.session_state.msgs)}", label_visibility="collapsed")
+    with cp:
         with st.popover("➕"):
-            up = st.file_uploader("Upload Medical PDF", type=['pdf'])
+            up = st.file_uploader("Upload PDF", type=['pdf'])
             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("PDF Context Extracted!") #
-    with col_q: 
-        q = st.chat_input("Enter clinical query...")
-
-    # Stable Processing Logic
-    final_q = None
-    if q: final_q = q
-    elif v:
-        v_hash = hashlib.md5(v.getvalue()).hexdigest()
-        if v_hash != st.session_state.last_voice_hash:
-            try:
-                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 = v_hash
-            except: st.error("Mic error. Please try again.")
-
+                    st.success("PDF Extracted") #
+    with cq: q = st.chat_input("Enter clinical query...")
+
+    # Stable AI Processing
+    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
     if final_q:
         st.session_state.msgs.append({"role": "user", "content": final_q})
-        save_history(st.session_state.username, final_q, "Patient") #
-        sys_p = "You are a Medical AI. Answer clinical queries. Use PDF data if present. Refuse non-medical topics."
-        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"CTX: {st.session_state.active_doc}"}] + st.session_state.msgs)
+        ans = Groq(api_key=GROQ_API_KEY).chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": "Medical AI"}, {"role": "system", "content": f"CTX: {st.session_state.active_doc}"}] + st.session_state.msgs)
         st.session_state.msgs.append({"role": "assistant", "content": ans.choices[0].message.content})
-        save_history(st.session_state.username, ans.choices[0].message.content, "AI") #
         st.rerun()
 
-# --- 7. ADDITIONAL TOOLS ---
-elif nav == "🧪 Health Lab":
-    st.markdown("### 🧪 Health Diagnostics")
-    tool = st.selectbox("Select Tool", ["BMI Analyzer", "Glucose Tracker", "Heart Simulator"])
-    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={'bar':{'color':"#10b981"}})))
-    elif tool == "Glucose Tracker":
-        
-        st.area_chart(pd.DataFrame(np.random.randn(20, 1).cumsum() + 100))
-    elif tool == "Heart Simulator":
-        
-        hr = st.slider("BPM", 40, 180, 72)
-        y = np.sin(2 * np.pi * (hr/60) * np.linspace(0, 2, 200))
-        st.plotly_chart(go.Figure(data=go.Scatter(y=y, mode='lines', line=dict(color='#ff4b4b'))))
-
-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)
-        st_folium(m, width=1000, height=500)
-
 elif nav == "📞 Video Consult":
-    st.markdown("### 📞 Specialist Live Call")
+    st.markdown("### 📞 Specialist Call Request")
     db = load_db(USER_DB, ["username", "role"])
-    doctors = db[db['role'] == 'Doctor']['username'].tolist() #
-    sel_doc = st.selectbox("Select Registered Specialist", doctors)
-    if st.button("Start Meeting"):
+    docs = db[db['role'] == 'Doctor']['username'].tolist() # Visibility of registered doctors
+    sel_doc = st.selectbox("Select Specialist", docs)
+    if st.button("Send Request & Join Room"):
         room = f"IntelliCare-{st.session_state.username}-{sel_doc}"
-        st.components.v1.html(f'<iframe src="https://meet.jit.si/{room}" width="100%" height="600px"></iframe>', height=650)
+        send_call_request(st.session_state.username, sel_doc, room) # Alert Doctor
+        log_call(st.session_state.username, sel_doc, room) # Log History
+        st.session_state.pat_room = room
+    if "pat_room" in st.session_state:
+        st.components.v1.html(f'<iframe src="https://meet.jit.si/{st.session_state.pat_room}" width="100%" height="600px"></iframe>', height=650)
 
-elif nav in ["📜 My History", "📜 Session Logs", "📋 Patient Archive"]:
-    st.markdown("### 📜 Clinical History Logs")
-    history = load_db(HISTORY_DB, ["Time", "User", "Message", "Role"])
-    if st.session_state.role == "Patient":
-        st.dataframe(history[history['User'] == st.session_state.username], use_container_width=True) #
-    else:
-        st.dataframe(history, use_container_width=True) #
+elif nav == "🧪 Health Lab":
+    st.markdown("### 🧪 Diagnostics")
+    w, h = st.number_input("Weight (kg)", 70), st.number_input("Height (cm)", 175)
+    st.metric("BMI", round(w / ((h/100)**2), 1))
+
+# --- 7. DOCTOR FEATURES ---
+elif nav == "🖥️ Consultation Desk":
+    st.markdown("### 🖥️ Clinical 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"🔔 Call request from **{my_calls.iloc[0]['Caller']}**")
+        if st.button("✅ Accept & Join"):
+            st.session_state.doc_room = my_calls.iloc[0]['RoomID']
+    else: st.write("Waiting for requests...")
+    if "doc_room" in st.session_state:
+        st.components.v1.html(f'<iframe src="https://meet.jit.si/{st.session_state.doc_room}" width="100%" height="600px"></iframe>', height=650)
+
+# --- 8. CALL HISTORY PORTAL ---
+elif nav in ["📜 Call History", "📜 Call Logs"]:
+    st.markdown("### 📜 Professional Call Logs")
+    history = load_db(CALL_LOG_DB, ["Time", "Caller", "Receiver", "RoomID"]) # Call logs only
+    filter_col = 'Caller' if st.session_state.role == "Patient" else 'Receiver'
+    st.dataframe(history[history[filter_col] == st.session_state.username], use_container_width=True)