Update src/streamlit_app.py

src/streamlit_app.py

@@ -8,65 +8,43 @@ import time
 # --- UI CONFIGURATION ---
 st.set_page_config(page_title="NEURAL-X | AI Classifier", layout="wide", initial_sidebar_state="expanded")
 
-# --- CUSTOM CSS (The Secret Sauce) ---
+# --- CUSTOM CSS ---
 st.markdown("""
     <style>
-    /* Main Background */
-    .stApp {
-        background: radial-gradient(circle at top right, #1e293b, #0f172a);
-        color: #f8fafc;
-    }
+    .stApp { background: radial-gradient(circle at top right, #1e293b, #0f172a); color: #f8fafc; }
+    h1 { font-family: 'Inter', sans-serif; font-weight: 800; background: -webkit-linear-gradient(#38bdf8, #818cf8); -webkit-background-clip: text; -webkit-text-fill-color: transparent; letter-spacing: -1px; }
     
-    /* Header Styling */
-    h1 {
-        font-family: 'Inter', sans-serif;
-        font-weight: 800;
-        background: -webkit-linear-gradient(#38bdf8, #818cf8);
-        -webkit-background-clip: text;
-        -webkit-text-fill-color: transparent;
-        letter-spacing: -1px;
+    /* FIX 1: STOP TEXT AREA OVERFLOW */
+    div.stTextArea { 
+        width: 100% !important; 
+        box-sizing: border-box !important;
     }
-
-    /* Glassmorphism Cards */
-    div[data-testid="stVerticalBlock"] > div:has(div.stTextArea) {
-        background: rgba(255, 255, 255, 0.03);
-        padding: 2rem;
-        border-radius: 20px;
-        border: 1px solid rgba(255, 255, 255, 0.1);
+    div[data-baseweb="textarea"] { 
+        background: rgba(255, 255, 255, 0.03) !important; 
+        border: 1px solid rgba(255, 255, 255, 0.1) !important; 
+        border-radius: 15px !important; 
         backdrop-filter: blur(10px);
     }
+    textarea { color: white !important; }
 
-    /* Button Styling */
-    .stButton > button {
-        width: 100%;
-        background: linear-gradient(90deg, #6366f1 0%, #a855f7 100%);
-        color: white;
-        border: none;
-        padding: 0.75rem;
-        border-radius: 12px;
-        font-weight: 600;
-        transition: all 0.3s ease;
-        text-transform: uppercase;
-        letter-spacing: 1px;
-    }
-    .stButton > button:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 10px 20px rgba(99, 102, 241, 0.4);
-    }
-
-    /* Result Metric Styling */
-    [data-testid="stMetricValue"] {
-        color: #38bdf8;
-        font-size: 2.5rem !important;
+    /* FIX 2: FORCE FULL LABEL NAMES TO WRAP, NOT TRUNCATE */
+    [data-testid="stMetricValue"] { 
+        color: #38bdf8; 
+        font-size: 2rem !important; 
+        white-space: normal !important; 
+        word-wrap: break-word !important;
+        line-height: 1.2;
     }
+    
+    .stButton > button { width: 100%; background: linear-gradient(90deg, #6366f1 0%, #a855f7 100%); color: white; border: none; padding: 0.75rem; border-radius: 12px; font-weight: 600; transition: all 0.3s ease; text-transform: uppercase; letter-spacing: 1px; }
+    .stButton > button:hover { transform: translateY(-2px); box-shadow: 0 10px 20px rgba(99, 102, 241, 0.4); }
     </style>
     """, unsafe_allow_html=True)
 
 # --- AI BACKEND ---
 @st.cache_resource(show_spinner=False)
 def load_ai():
-    device = 0 if torch.cuda.is_available() else -1
-    return pipeline("zero-shot-classification", model="facebook/bart-large-mnli", device=device)
+    return pipeline("zero-shot-classification", model="cross-encoder/nli-distilroberta-base", device=-1)
 
 classifier = load_ai()
 
@@ -94,31 +72,30 @@ st.write("#### Enterprise-grade semantic analysis powered by Deep Learning.")
 col1, col2 = st.columns([1.2, 0.8], gap="large")
 
 with col1:
-    text_input = st.text_area("INPUT STREAM", height=250, placeholder="Paste raw text, news articles, or transcripts here...")
+    text_input = st.text_area("INPUT STREAM", height=250, placeholder="Paste raw text here...")
     analyze_btn = st.button("Execute Neural Scan")
 
 with col2:
     if analyze_btn:
         if not text_input.strip():
-            st.warning("⚠️ Neural input buffer empty. Please provide text.")
+            st.warning("⚠️ Neural input buffer empty.")
         else:
             with st.spinner("Processing Tensors..."):
                 start_time = time.time()
                 result = classifier(text_input, HIDDEN_LABELS, multi_label=True)
                 end_time = time.time()
                 
-                # Dynamic Logic
                 valid_pairs = [(l, s) for l, s in zip(result['labels'], result['scores']) if s >= 0.60]
                 if not valid_pairs:
                     valid_pairs = [(result['labels'][0], result['scores'][0])]
                 
-                # Metrics
                 st.subheader("Analysis Results")
                 m1, m2 = st.columns(2)
+                
+                # Metric will now properly wrap text instead of "..."
                 m1.metric("Top Category", valid_pairs[0][0])
                 m2.metric("Inference Time", f"{round(end_time - start_time, 2)}s")
                 
-                # Chart
                 df = pd.DataFrame({
                     "Concept": [p[0] for p in valid_pairs],
                     "Confidence": [p[1] for p in valid_pairs]
@@ -127,10 +104,12 @@ with col2:
                 fig = px.bar(df, x="Confidence", y="Concept", orientation='h',
                              color="Confidence", color_continuous_scale="Tealgrn",
                              template="plotly_dark")
+                             
+                # FIX 3: Force Plotly to give long labels enough margin space
+                fig.update_yaxes(automargin=True)
                 fig.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
                 st.plotly_chart(fig, use_container_width=True)
                 
-                # Add to History
                 st.session_state.history.append({"label": valid_pairs[0][0], "text": text_input})
     else:
         st.info("💡 Enter text and initiate scan to view semantic mapping.")