Update app.py

app.py

@@ -12,131 +12,152 @@ from groq import Groq
 from fpdf import FPDF
 from datetime import datetime
 
-# --- 1. CORE CONFIG ---
+# --- 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!")
+    st.error("⚠️ API Keys Missing! Please 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")
-
-# --- 2. PERSISTENCE ENGINES (CSVs for Records) ---
-HISTORY_FILE = "clinical_history.csv"
-
-def save_visit(doctor, room, status):
-    df = pd.read_csv(HISTORY_FILE) if os.path.exists(HISTORY_FILE) else pd.DataFrame(columns=["Time", "Doctor", "Room", "Status"])
-    new_data = pd.DataFrame([{"Time": datetime.now().strftime("%H:%M:%S"), "Doctor": doctor, "Room": room, "Status": status}])
-    pd.concat([df, new_data]).to_csv(HISTORY_FILE, index=False)
+st.set_page_config(page_title="MediScan Pro | Hassan Naseer", layout="wide", page_icon="🏥")
+
+# --- 2. PERSISTENT DATABASE ENGINE ---
+# Shared file for both Doctor and Patient portals
+RECORDS_FILE = "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)
 
 @st.cache_resource
-def get_db():
+def get_chroma_db():
     client = chromadb.PersistentClient(path="./med_vector_db")
     emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
-    main = client.get_or_create_collection(name="nutech_knowledge", embedding_function=emb_fn)
-    signal = client.get_or_create_collection(name="call_signals", embedding_function=emb_fn)
-    return main, signal
-
-main_col, signal_col = get_db()
-
-# --- 3. UI HELPERS ---
-def generate_pro_pdf(history):
-    pdf = FPDF()
-    pdf.add_page(); pdf.set_font("Arial", 'B', 16)
-    pdf.cell(200, 10, txt="NUTECH MEDISCAN CLINICAL REPORT", ln=True, align='C')
-    pdf.set_font("Arial", size=10); pdf.ln(10)
-    for m in history:
-        label = "PATIENT: " if m["role"] == "user" else "AI DOCTOR: "
-        pdf.set_font("Arial", 'B', 11); pdf.cell(200, 8, txt=label, ln=True)
-        pdf.set_font("Arial", size=10); pdf.multi_cell(0, 8, txt=m["content"].encode('latin-1', 'ignore').decode('latin-1'))
-    return pdf.output(dest='S').encode('latin-1')
-
-# --- 4. AUTH & NAVIGATION ---
+    # 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.title("🏥 MediScan Enterprise")
-    role = st.selectbox("Role", ["Patient", "Doctor"])
-    pwd = st.text_input("Password", type="password")
-    if st.button("Login"):
-        if (role == "Patient" and pwd == "p123") or (role == "Doctor" and pwd == "d123"):
-            st.session_state.auth = True; st.session_state.role = role; st.rerun()
+    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)
     st.stop()
 
+# --- 5. NAVIGATION ---
 with st.sidebar:
-    st.title(f"👤 {st.session_state.role}")
+    st.title(f"👤 {st.session_state.role} Portal")
     if st.button("Logout"): st.session_state.auth = False; st.rerun()
     st.divider()
     if st.session_state.role == "Patient":
-        nav = st.radio("Menu", ["💬 AI Chat", "🧪 Diagnostics", "📞 Video Call", "📸 Vision Scanner", "📜 My Records"])
+        nav = st.radio("Services", ["💬 AI Chat & Docs", "🧪 Diagnostics", "📞 Contact Doctor", "📸 Vision Scanner", "📜 My Records"])
     else:
-        nav = st.radio("Menu", ["🖥️ Consultation Desk", "📋 Patient Logs"])
+        nav = st.radio("Doctor Desk", ["🖥️ Live Tele-Consult", "📋 Global Patient Logs"])
+    st.divider()
+    st.info("System Status: Synchronized (V3.0)")
 
-# --- 5. PATIENT PORTAL ---
+# --- 6. PATIENT PORTAL MODULE ---
 if st.session_state.role == "Patient":
-    if nav == "💬 AI Chat":
-        st.header("💬 AI Assistant & Document Uploader")
-        if st.session_state.msgs:
-            st.download_button("📄 Export Clinical PDF", data=generate_pro_pdf(st.session_state.msgs), file_name="MedReport.pdf")
+    if nav == "💬 AI Chat & Docs":
+        st.header("💬 Clinical AI Assistant")
         for m in st.session_state.msgs: st.chat_message(m["role"]).write(m["content"])
         
-        q = st.chat_input("Enter symptoms...")
+        q = st.chat_input("Explain your symptoms...")
         c1, c2 = st.columns(2)
-        with c1: v = st.audio_input("Voice Input", key=f"v_{len(st.session_state.msgs)}")
-        with c2: up = st.file_uploader("Upload Lab PDF/IMG", type=['pdf', 'png', 'jpg'])
+        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'])
         
-        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})
-            res = main_col.query(query_texts=[fq], n_results=1)
-            ctx = res['documents'][0][0] if res['documents'] else ""
+        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"
             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("🧪 Lab Metrics")
-        bpm = st.slider("BPM", 40, 200, 72); st.metric("Heart Rate", bpm)
-        sug = st.slider("Sugar", 50, 400, 95); st.metric("Glucose", sug)
-
-    elif nav == "📞 Video Call":
-        st.header("📞 Request Live Call")
-        doc = st.selectbox("Doctor", ["Dr. Ahmed", "Dr. Sara"])
-        if st.button("Send Request"):
+        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 == "📞 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}|{room}|{datetime.now().strftime('%H:%M')}"], 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" allow="camera; microphone;"></iframe>', height=600)
+            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)
 
     elif nav == "📜 My Records":
-        st.header("📜 Visit History")
-        if os.path.exists(HISTORY_FILE): st.table(pd.read_csv(HISTORY_FILE))
+        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.")
 
-# --- 6. DOCTOR PORTAL ---
+# --- 7. DOCTOR PORTAL MODULE ---
 elif st.session_state.role == "Doctor":
-    if nav == "🖥️ Consultation Desk":
-        st.header("🖥️ Call Dashboard")
+    if nav == "🖥️ Live Tele-Consult":
+        st.header("🖥️ Consultation Dashboard")
         res = signal_col.get(ids=["latest"])
         if res['documents']:
             parts = res['documents'][0].split("|")
-            st.warning(f"🔔 CALL: For {parts[1]} at {parts[3]}")
-            if st.button("Join"):
+            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("End & Archive"):
-                save_visit(parts[1], parts[2], "Completed")
-                signal_col.delete(ids=["latest"]); st.rerun()
+            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.")