Update src/streamlit_app.py

src/streamlit_app.py

@@ -6,8 +6,9 @@ from PIL import Image
 from huggingface_hub import InferenceClient
 
 # -----------------------------------------------------------------------------
-# 0. AUTO-FIX FOR UPLOAD ERROR (RUNS FIRST)
+# 0. AUTO-FIX FOR UPLOAD ERROR (CRITICAL STEP)
 # -----------------------------------------------------------------------------
+# This code runs instantly to create the config file needed for uploads.
 config_dir = ".streamlit"
 if not os.path.exists(config_dir):
     os.makedirs(config_dir)
@@ -20,18 +21,16 @@ with open(os.path.join(config_dir, "config.toml"), "w") as f:
 st.set_page_config(page_title="SHINUI | Intelligent Care", page_icon="✨", layout="wide")
 
 # RETRIEVE API KEY
-# Make sure "HF_TOKEN" is in your Space Settings -> Secrets
 HF_TOKEN = os.environ.get("HF_TOKEN")
-
 if not HF_TOKEN:
     st.error("⚠️ API Key missing! Please add 'HF_TOKEN' in Space Settings > Secrets.")
     st.stop()
 
-# INITIALIZE CLIENT
+# INITIALIZE CLIENT (Ungated Models)
 client = InferenceClient(token=HF_TOKEN)
 
 # -----------------------------------------------------------------------------
-# 2. STATE MANAGEMENT (LOGIN & HISTORY)
+# 2. STATE MANAGEMENT
 # -----------------------------------------------------------------------------
 if 'page' not in st.session_state: st.session_state.page = 'landing'
 if 'logged_in' not in st.session_state: st.session_state.logged_in = False
@@ -40,59 +39,34 @@ if 'history' not in st.session_state: st.session_state.history = []
 if 'result' not in st.session_state: st.session_state.result = None
 
 # -----------------------------------------------------------------------------
-# 3. THE BRAIN (Llama 3.2 Logic)
+# 3. THE BRAIN (Simplified Logic)
 # -----------------------------------------------------------------------------
 def get_ai_insight(input_type, content):
     """
-    Handles AI processing using Llama 3.2 Vision Instruct.
+    Handles Text and Image processing using open-source models.
     """
-    prompt_base = "You are SHINUI, a professional medical AI assistant. Analyze the following input and provide a concise risk assessment and next steps."
-
     try:
-        # A. VISION (Llama 3.2 Vision)
+        # A. VISION (BLIP - Open Source)
         if input_type == "Image":
-            messages = [
-                {
-                    "role": "user", 
-                    "content": [
-                        {"type": "image"},
-                        {"type": "text", "text": prompt_base}
-                    ]
-                }
-            ]
-            # Pass image directly to the client
+            # 1. Describe image
+            desc = client.image_to_text(model="Salesforce/blip-image-captioning-large", image=content)
+            
+            # 2. Analyze medically
+            prompt = f"I have a medical image showing: '{desc}'. Please analyze this medically for risks."
             response = client.chat_completion(
-                model="meta-llama/Llama-3.2-11B-Vision-Instruct",
-                messages=messages,
-                max_tokens=500,
-                image=content # The PIL Image object
+                model="HuggingFaceH4/zephyr-7b-beta",
+                messages=[{"role": "user", "content": prompt}],
+                max_tokens=400
             )
             return response.choices[0].message.content
 
-        # B. AUDIO (Whisper -> Llama)
-        elif input_type == "Audio":
-            # 1. Transcribe
-            transcription = client.automatic_speech_recognition(
-                model="openai/whisper-large-v3-turbo",
-                audio=content
-            ).text
-            
-            # 2. Analyze Text
-            messages = [{"role": "user", "content": f"{prompt_base} Patient speech: '{transcription}'"}]
-            analysis = client.chat_completion(
-                model="meta-llama/Llama-3.2-11B-Vision-Instruct",
-                messages=messages,
-                max_tokens=500
-            )
-            return f"**Transcript:** '{transcription}'\n\n**Analysis:** {analysis.choices[0].message.content}"
-
-        # C. TEXT (Llama)
+        # B. TEXT (Zephyr - Open Source)
         elif input_type == "Text":
-            messages = [{"role": "user", "content": f"{prompt_base} Patient notes: '{content}'"}]
+            prompt = f"Analyze these symptoms medically: '{content}'"
             response = client.chat_completion(
-                model="meta-llama/Llama-3.2-11B-Vision-Instruct",
-                messages=messages,
-                max_tokens=500
+                model="HuggingFaceH4/zephyr-7b-beta",
+                messages=[{"role": "user", "content": prompt}],
+                max_tokens=400
             )
             return response.choices[0].message.content
 
@@ -108,8 +82,7 @@ st.markdown("""
     .stApp {
         background-color: #020617;
         background-image: radial-gradient(circle at 50% 0%, #1e293b 0%, #020617 70%);
-        font-family: 'Plus Jakarta Sans', sans-serif;
-        color: #f8fafc;
+        font-family: 'Plus Jakarta Sans', sans-serif; color: #f8fafc;
     }
     .shinui-card {
         background: rgba(30, 41, 59, 0.4); border: 1px solid rgba(148, 163, 184, 0.1);
@@ -125,7 +98,7 @@ st.markdown("""
 """, unsafe_allow_html=True)
 
 # -----------------------------------------------------------------------------
-# 5. HELPER FUNCTIONS
+# 5. NAVIGATION
 # -----------------------------------------------------------------------------
 def nav_to(page):
     st.session_state.page = page
@@ -155,21 +128,47 @@ def show_landing():
             Medical Intelligence.<br><span style='color:#38bdf8;'>Simplified.</span>
         </h1>
         <p style='font-size: 1.2rem; color: #94a3b8; margin-bottom: 40px;'>
-            SHINUI uses Llama 3.2 Vision AI to analyze medical data instantly.
+            SHINUI analyzes medical data instantly using open AI.
         </p>
         """, unsafe_allow_html=True)
         b1, b2 = st.columns([1, 2])
         with b1: 
             if st.button("Sign In"): nav_to('login')
+        with b2:
+            if st.button("About SHINUI"): nav_to('about')
     
     with c2:
         st.markdown("""
         <div class='shinui-card'>
-            <h3>🧬 Multimodal Brain</h3>
-            <p style='color:#94a3b8;'>Powered by Meta Llama 3.2 Vision.</p>
+            <h3>🧬 Dual-Core Engine</h3>
+            <p style='color:#94a3b8;'>Text & Vision Analysis.</p>
         </div>
         """, unsafe_allow_html=True)
 
+# --- ABOUT PAGE ---
+def show_about():
+    if st.button("← Back Home"): nav_to('landing')
+    st.markdown("<br>", unsafe_allow_html=True)
+    st.markdown("""
+    <div class='shinui-card'>
+        <h2 style='color:#38bdf8'>About SHINUI</h2>
+        <p style='font-size:1.1rem; line-height:1.6'>
+            SHINUI is an AI-powered medical assistant designed to bring clarity to complex health data.
+            Whether it's a handwritten note, a lab result, or a pill bottle, SHINUI helps you understand it.
+        </p>
+        <hr style='border-color:#333'>
+        <h3>Our Mission</h3>
+        <p>To democratize access to medical understanding through simple, secure AI.</p>
+        <br>
+        <h3>How it Works</h3>
+        <ol>
+            <li><b>Upload:</b> Send an image or text description.</li>
+            <li><b>Process:</b> Our AI analyzes the visual and textual data.</li>
+            <li><b>Result:</b> Get a clear, jargon-free explanation.</li>
+        </ol>
+    </div>
+    """, unsafe_allow_html=True)
+
 # --- LOGIN ---
 def show_login():
     c1, c2, c3 = st.columns([1,1,1])
@@ -187,11 +186,13 @@ def show_login():
 
 # --- DASHBOARD ---
 def show_dashboard():
-    # SIDEBAR
+    # SIDEBAR NAV
     with st.sidebar:
         st.markdown(f"### 👤 {st.session_state.user_email}")
+        if st.button("Home / Dashboard"): nav_to('dashboard')
+        if st.button("About"): nav_to('about_internal')
         st.markdown("---")
-        st.write("HISTORY LOG")
+        st.write("HISTORY")
         if st.session_state.history:
             for h in reversed(st.session_state.history):
                 st.markdown(f"<div style='font-size:0.8rem; padding:5px; border-left:2px solid #38bdf8; margin-bottom:5px;'>{h[:50]}...</div>", unsafe_allow_html=True)
@@ -202,7 +203,7 @@ def show_dashboard():
 
     # MAIN UI
     st.title("Diagnostic Interface")
-    t1, t2, t3 = st.tabs(["📷 Vision", "🎙️ Voice", "📝 Text"])
+    t1, t2 = st.tabs(["📷 Image Scan", "📝 Text Analysis"])
 
     # TAB 1: VISION
     with t1:
@@ -216,20 +217,8 @@ def show_dashboard():
                 st.session_state.history.append(res)
         st.markdown("</div>", unsafe_allow_html=True)
 
-    # TAB 2: VOICE
+    # TAB 2: TEXT
     with t2:
-        st.markdown("<div class='shinui-card'>", unsafe_allow_html=True)
-        audio_input = st.audio_input("Record Symptoms")
-        if audio_input:
-            if st.button("Analyze Recording"):
-                with st.spinner("Transcribing & Analyzing..."):
-                    res = get_ai_insight("Audio", audio_input.read())
-                    st.session_state.result = res
-                    st.session_state.history.append(res)
-        st.markdown("</div>", unsafe_allow_html=True)
-
-    # TAB 3: TEXT
-    with t3:
         st.markdown("<div class='shinui-card'>", unsafe_allow_html=True)
         txt = st.text_area("Clinical Notes")
         if txt and st.button("Analyze Notes"):
@@ -248,11 +237,34 @@ def show_dashboard():
         </div>
         """, unsafe_allow_html=True)
 
+# --- INTERNAL ABOUT (For logged in users) ---
+def show_about_internal():
+    with st.sidebar:
+        if st.button("← Back to Dashboard"): nav_to('dashboard')
+    
+    st.markdown("""
+    <div class='shinui-card'>
+        <h2 style='color:#38bdf8'>System Information</h2>
+        <p>You are currently logged into the SHINUI Diagnostic System.</p>
+        <p>Version: 2.0.1 (Stable)</p>
+        <hr style='border-color:#333'>
+        <h3>Capabilities</h3>
+        <ul>
+            <li><b>Visual Analysis:</b> Uses Computer Vision to interpret X-rays, labels, and scans.</li>
+            <li><b>Text Processing:</b> Uses NLP to understand clinical notes and symptoms.</li>
+        </ul>
+    </div>
+    """, unsafe_allow_html=True)
+
 # -----------------------------------------------------------------------------
 # 7. ROUTER
 # -----------------------------------------------------------------------------
 if st.session_state.page == 'landing': show_landing()
+elif st.session_state.page == 'about': show_about()
 elif st.session_state.page == 'login': show_login()
 elif st.session_state.page == 'dashboard':
     if st.session_state.logged_in: show_dashboard()
+    else: nav_to('login')
+elif st.session_state.page == 'about_internal':
+    if st.session_state.logged_in: show_about_internal()
     else: nav_to('login')