Update app.py

app.py

@@ -11,80 +11,93 @@ from PIL import Image
 from groq import Groq
 from fpdf import FPDF
 from datetime import datetime
+import hashlib
 
-# --- 1. CORE SYSTEM CONFIG ---
+# --- 1. SECURE CONFIGURATION ---
 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 add them to Space Secrets.")
+    st.error("⚠️ API Keys Missing! Add them to 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="MediScan Pro | Hassan Naseer", layout="wide", page_icon="🏥")
+st.set_page_config(page_title="HealthConnect | Hassan Naseer", layout="wide", page_icon="🏥")
 
-# --- 2. PERSISTENT DATABASE ENGINE ---
-# Shared file for both Doctor and Patient portals
-RECORDS_FILE = "clinical_records.csv"
+# --- 2. PERSISTENT STORAGE (USER & CLINICAL DB) ---
+USER_DB = "users_secure.csv"
+HISTORY_DB = "permanent_clinical_records.csv"
 
-def save_clinical_record(doctor, room, chat_history):
-    # Convert chat history into a readable summary string
-    chat_summary = " | ".join([f"{m['role'].upper()}: {m['content'][:50]}..." for m in chat_history])
-    df = pd.read_csv(RECORDS_FILE) if os.path.exists(RECORDS_FILE) else pd.DataFrame(columns=["Timestamp", "Doctor", "RoomID", "Summary"])
-    
-    new_entry = pd.DataFrame([{
-        "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
-        "Doctor": doctor,
-        "RoomID": room,
-        "Summary": chat_summary
-    }])
-    pd.concat([df, new_entry]).to_csv(RECORDS_FILE, index=False)
+def hash_pass(password):
+    return hashlib.sha256(str.encode(password)).hexdigest()
+
+def load_db(file, cols):
+    if os.path.exists(file): return pd.read_csv(file)
+    return pd.DataFrame(columns=cols)
 
+def save_user(username, password, role):
+    df = load_db(USER_DB, ["username", "password", "role"])
+    if username in df['username'].values: return False
+    new_user = pd.DataFrame([{"username": username, "password": hash_pass(password), "role": role}])
+    pd.concat([df, new_user]).to_csv(USER_DB, index=False)
+    return True
+
+# --- 3. VECTOR DATABASE ENGINE ---
 @st.cache_resource
-def get_chroma_db():
+def get_db():
     client = chromadb.PersistentClient(path="./med_vector_db")
     emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
-    # Medical Knowledge Base
-    main_col = client.get_or_create_collection(name="nutech_final_prod", embedding_function=emb_fn)
-    # Live Signaling for Video Calls
-    signal_col = client.get_or_create_collection(name="call_signals_prod", embedding_function=emb_fn)
-    return main_col, signal_col
-
-main_col, signal_col = get_chroma_db()
-
-# --- 3. SESSION STATE ---
-if "auth" not in st.session_state: st.session_state.auth = False
-if "msgs" not in st.session_state: st.session_state.msgs = []
-if "v_hash" not in st.session_state: st.session_state.v_hash = None
-
-# --- 4. AUTHENTICATION GATE ---
-if not st.session_state.auth:
-    st.markdown("<h1 style='text-align: center;'>🏥 MediScan Enterprise</h1>", unsafe_allow_html=True)
-    c1, c2, c3 = st.columns([1, 1.5, 1])
-    with c2:
-        st.markdown('<div style="background:white; padding:20px; border-radius:10px; border:1px solid #ddd;">', unsafe_allow_html=True)
-        role = st.selectbox("I am a:", ["Patient", "Doctor"])
-        pwd = st.text_input("Access Key", type="password")
-        if st.button("Enter Portal"):
-            if (role == "Patient" and pwd == "p123") or (role == "Doctor" and pwd == "d123"):
-                st.session_state.auth = True; st.session_state.role = role; st.rerun()
-            else: st.error("Incorrect Password.")
-        st.markdown('</div>', unsafe_allow_html=True)
+    main = client.get_or_create_collection(name="nutech_final_rag", embedding_function=emb_fn)
+    signal = client.get_or_create_collection(name="global_call_signals", embedding_function=emb_fn)
+    return main, signal
+
+main_col, signal_col = get_db()
+
+# --- 4. AUTHENTICATION GATEWAY ---
+if "logged_in" not in st.session_state: st.session_state.logged_in = False
+
+if not st.session_state.logged_in:
+    st.markdown("<h1 style='text-align: center;'>🏥 HealthConnect Enterprise</h1>", unsafe_allow_html=True)
+    tab1, tab2 = st.tabs(["🔐 Login", "📝 Create Account"])
+    
+    with tab2:
+        new_u = st.text_input("Choose Username")
+        new_p = st.text_input("Choose Password", type="password")
+        new_role = st.selectbox("I am a:", ["Patient", "Doctor"])
+        if st.button("Register Account"):
+            if save_user(new_u, new_p, new_role): st.success("Account Created! Please Login.")
+            else: st.error("Username already taken.")
+
+    with tab1:
+        u = st.text_input("Username")
+        p = st.text_input("Password", type="password")
+        if st.button("Login"):
+            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 = True
+                st.session_state.username = u
+                st.session_state.role = match.iloc[0]['role']
+                st.session_state.msgs = [] # Fresh session chat
+                st.rerun()
+            else: st.error("Invalid credentials.")
     st.stop()
 
-# --- 5. NAVIGATION ---
+# --- 5. PORTAL NAVIGATION ---
 with st.sidebar:
-    st.title(f"👤 {st.session_state.role} Portal")
-    if st.button("Logout"): st.session_state.auth = False; st.rerun()
+    st.markdown(f"### Welcome, **{st.session_state.username}**")
+    st.caption(f"Role: {st.session_state.role}")
+    if st.button("Sign Out"):
+        st.session_state.logged_in = False; st.rerun()
     st.divider()
     if st.session_state.role == "Patient":
-        nav = st.radio("Services", ["💬 AI Chat & Docs", "🧪 Diagnostics", "📞 Contact Doctor", "📸 Vision Scanner", "📜 My Records"])
+        nav = st.radio("Menu", ["💬 AI Chat & Docs", "🧪 Health Lab", "📞 Contact Doctor", "📸 Vision Scanner", "📜 My Records"])
     else:
-        nav = st.radio("Doctor Desk", ["🖥️ Live Tele-Consult", "📋 Global Patient Logs"])
+        nav = st.radio("Menu", ["🖥️ Tele-Consult Desk", "📋 Patient Archives"])
     st.divider()
-    st.info("System Status: Synchronized (V3.0)")
+    st.info("System: Production Ready")
 
 # --- 6. PATIENT PORTAL MODULE ---
 if st.session_state.role == "Patient":
@@ -92,72 +105,76 @@ if st.session_state.role == "Patient":
         st.header("💬 Clinical AI Assistant")
         for m in st.session_state.msgs: st.chat_message(m["role"]).write(m["content"])
         
-        q = st.chat_input("Explain your symptoms...")
+        q = st.chat_input("Enter symptoms...")
         c1, c2 = st.columns(2)
-        with c1: v_in = st.audio_input("Voice Input", key=f"v_{len(st.session_state.msgs)}")
-        with c2: doc_in = st.file_uploader("Upload Lab Report (PDF/IMG)", type=['pdf', 'png', 'jpg'])
+        with c1: v = st.audio_input("Voice Input", key=f"v_{len(st.session_state.msgs)}")
+        with c2: up = st.file_uploader("Upload Medical PDF/IMG", type=['pdf', 'png', 'jpg'])
         
-        final_q = q if q else None
-        if doc_in:
-            with st.spinner("Analyzing Document..."):
-                if doc_in.type == "application/pdf":
-                    with pdfplumber.open(doc_in) as pdf: final_q = "Summarize: " + " ".join([p.extract_text() for p in pdf.pages if p.extract_text()])
-                else: final_q = "Analyze Scan: " + " ".join(easyocr.Reader(['en']).readtext(np.array(Image.open(doc_in)), detail=0))
-        elif v_in and v_in.size > 0:
-            vh = hash(v_in.getvalue())
-            if st.session_state.v_hash != vh:
-                final_q = Groq(api_key=GROQ_API_KEY).audio.transcriptions.create(file=("a.wav", v_in.getvalue()), model="whisper-large-v3", response_format="text")
-                st.session_state.v_hash = vh
-
-        if final_q:
-            st.session_state.msgs.append({"role": "user", "content": final_q})
-            res = main_col.query(query_texts=[final_q], n_results=1)
-            ctx = res['documents'][0][0] if res['documents'] else "N/A"
+        fq = q if q else None
+        if up:
+            with st.spinner("Processing..."):
+                if up.type == "application/pdf":
+                    with pdfplumber.open(up) as f: fq = "Review PDF: " + " ".join([p.extract_text() for p in f.pages if p.extract_text()])
+                else: fq = "Review Scan: " + " ".join(easyocr.Reader(['en']).readtext(np.array(Image.open(up)), detail=0))
+        elif v:
+            fq = Groq(api_key=GROQ_API_KEY).audio.transcriptions.create(file=("a.wav", v.getvalue()), model="whisper-large-v3", response_format="text")
+
+        if fq:
+            st.session_state.msgs.append({"role": "user", "content": fq})
+            # --- INDEX ERROR SAFETY FIX ---
+            res = main_col.query(query_texts=[fq], n_results=1)
+            if res.get('documents') and len(res['documents']) > 0 and len(res['documents'][0]) > 0:
+                ctx = res['documents'][0][0]
+            else:
+                ctx = "No specific match in local database."
+            # ------------------------------
             ans = Groq(api_key=GROQ_API_KEY).chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"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()
 
-    elif nav == "🧪 Diagnostics":
-        st.header("🧪 Interactive Health Metrics")
-        col1, col2 = st.columns(2)
-        with col1:
-            bpm = st.slider("Current Heart Rate (BPM)", 40, 200, 72); st.metric("Pulse", bpm)
-        with col2:
-            sugar = st.slider("Blood Glucose (mg/dL)", 50, 400, 95); st.metric("Glucose", sugar)
-        st.image("https://www.verywellfit.com/thmb/p2O_L3uS9m7O3r4E1uG2F3G_Y3I=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/HeartRateChart-5b8d5a1bc9e77c00507a2a1a.jpg", use_container_width=True)
+    elif nav == "🧪 Health Lab":
+        st.header("🧪 Interactive Diagnostics")
+        bpm = st.slider("Heart Rate (BPM)", 40, 200, 72); st.metric("Pulse", bpm)
+        sugar = st.slider("Blood Glucose (mg/dL)", 50, 400, 95); st.metric("Level", sugar)
 
     elif nav == "📞 Contact Doctor":
-        st.header("📞 Start Video Consultation")
-        doc_name = st.selectbox("Available Specialist", ["Dr. Ahmed", "Dr. Sara"])
-        if st.button("Request Meeting"):
-            room = f"NUTECH-{np.random.randint(100,999)}"
-            signal_col.upsert(documents=[f"CALL|{doc_name}|{room}|{datetime.now().strftime('%H:%M')}"], ids=["latest"])
-            st.session_state.active_room = room
-            st.success("Calling Specialist...")
-        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" allow="camera; microphone;"></iframe>', height=600)
+        st.header("📞 Request Live Consultation")
+        doc = st.selectbox("Choose Doctor", ["Dr_Ahmed", "Dr_Sara"])
+        if st.button("Start Connection"):
+            room = f"Room-{st.session_state.username}-{np.random.randint(100,999)}"
+            signal_col.upsert(documents=[f"CALL|{doc}|{room}|{st.session_state.username}"], ids=["latest"])
+            st.session_state.room = room
+        if "room" in st.session_state:
+            st.components.v1.html(f'<iframe src="https://meet.jit.si/{st.session_state.room}" width="100%" height="550px"></iframe>', height=600)
+
+    elif nav == "📸 Vision Scanner":
+        cam = st.camera_input("Scanner")
+        if cam:
+            rep = vision_model.generate_content(["ACT AS PHARMACIST: Salt, Dosage.", Image.open(cam)])
+            st.write(rep.text)
 
     elif nav == "📜 My Records":
-        st.header("📜 My Permanent Visit Records")
-        if os.path.exists(RECORDS_FILE): st.table(pd.read_csv(RECORDS_FILE))
-        else: st.info("No records found.")
+        st.header("📜 My Visit History")
+        df = load_db(HISTORY_DB, ["Time", "Patient", "Doctor", "Status"])
+        st.table(df[df['Patient'] == st.session_state.username])
 
 # --- 7. DOCTOR PORTAL MODULE ---
 elif st.session_state.role == "Doctor":
-    if nav == "🖥️ Live Tele-Consult":
-        st.header("🖥️ Consultation Dashboard")
+    if nav == "🖥️ Tele-Consult Desk":
+        st.header("🖥️ Active Incoming Desk")
         res = signal_col.get(ids=["latest"])
         if res['documents']:
             parts = res['documents'][0].split("|")
-            st.warning(f"🔔 INCOMING CALL: {parts[1]} is requesting a session.")
-            if st.button("Accept & Join"):
-                st.components.v1.html(f'<iframe src="https://meet.jit.si/{parts[2]}" width="100%" height="600px" allow="camera; microphone;"></iframe>', height=650)
-            if st.button("✅ Archive Session & Save Patient Logs"):
-                save_clinical_record(parts[1], parts[2], st.session_state.msgs)
-                signal_col.delete(ids=["latest"]); st.success("Session Archived!"); st.rerun()
-        else: st.info("Waiting for patient requests...")
-
-    elif nav == "📋 Global Patient Logs":
-        st.header("📋 Global Consultation History")
-        if os.path.exists(RECORDS_FILE): st.dataframe(pd.read_csv(RECORDS_FILE), use_container_width=True)
-        else: st.info("No global records found.")
+            st.warning(f"🔔 CALL: Incoming from {parts[3]}")
+            if st.button("Join Room"):
+                st.components.v1.html(f'<iframe src="https://meet.jit.si/{parts[2]}" width="100%" height="600px"></iframe>', height=650)
+            if st.button("✅ Archive Session"):
+                df = load_db(HISTORY_DB, ["Time", "Patient", "Doctor", "Status"])
+                new_row = pd.DataFrame([{"Time": datetime.now().strftime("%Y-%m-%d %H:%M"), "Patient": parts[3], "Doctor": st.session_state.username, "Status": "Completed"}])
+                pd.concat([df, new_row]).to_csv(HISTORY_DB, index=False)
+                signal_col.delete(ids=["latest"]); st.success("Session Saved!"); st.rerun()
+        else: st.info("Monitoring for patient requests...")
+
+    elif nav == "📋 Patient Archives":
+        st.header("📋 Global Clinical Records")
+        st.dataframe(load_db(HISTORY_DB, ["Time", "Patient", "Doctor", "Status"]), use_container_width=True)