Update src/streamlit_app.py

src/streamlit_app.py

@@ -9,7 +9,6 @@ from transformers import AutoModel
 
 st.set_page_config(page_title="Political Sentiment", page_icon="🇧🇩", layout="wide")
 
-
 class BanglaPoliticalNet(nn.Module):
     def __init__(self, num_classes=5):
         super().__init__()
@@ -39,7 +38,9 @@ class BanglaPoliticalNet(nn.Module):
             cnn_features.append(F.relu(cnn_out))
         
         cnn_concat = torch.cat(cnn_features, dim=-1)
-        attn_out, _ = self.attention(cnn_concat, cnn_concat, cnn_concat)
+        proj = nn.Linear(384, self.hidden_size).to(input_ids.device)
+        attn_input = proj(cnn_concat)
+        attn_out, _ = self.attention(attn_input, attn_input, attn_input)
         attn_pooled = attn_out[:, 0, :]
         
         logits = self.classifier(attn_pooled)
@@ -49,53 +50,203 @@ st.markdown("""
 <style>
     @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap');
     
-    html, body, [class*="css"] { font-family: 'Inter', sans-serif; color: #000000; }
-    .stApp { background-color: #f4f6f9; }
-    h1, h2, h3 { color: #111827 !important; }
-    .main-card { background-color: white; padding: 30px; border-radius: 15px; box-shadow: 0 4px 10px rgba(0,0,0,0.08); margin-bottom: 25px; text-align: center; border: 1px solid #e5e7eb; }
-    .result-title { color: #374151; font-size: 16px; text-transform: uppercase; letter-spacing: 1.5px; margin-bottom: 10px; font-weight: 700; }
-    .result-value { font-size: 48px; font-weight: 800; margin: 0; }
-    .section-header { font-size: 20px; font-weight: 700; color: #1f2937; margin-bottom: 20px; border-left: 5px solid #2563eb; padding-left: 10px; }
-    .model-card { background-color: white; padding: 20px; border-radius: 12px; box-shadow: 0 2px 5px rgba(0,0,0,0.05); margin-bottom: 15px; border: 1px solid #e5e7eb; }
-    .model-card:hover { transform: translateY(-3px); box-shadow: 0 8px 15px rgba(0,0,0,0.1); }
-    .model-name { color: #4b5563; font-size: 14px; font-weight: 600; margin-bottom: 8px; border-bottom: 2px solid #f3f4f6; padding-bottom: 5px; }
-    .prob-row { margin-bottom: 15px; }
-    .prob-label { font-size: 14px; color: #111827; font-weight: 600; margin-bottom: 5px; display: flex; justify-content: space-between; }
-    .prob-bar-bg { width: 100%; height: 10px; background-color: #e5e7eb; border-radius: 5px; overflow: hidden; }
-    .prob-bar-fill { height: 100%; border-radius: 5px; }
+    html, body, [class*="css"] {
+        font-family: 'Inter', sans-serif !important;
+        color: #1f2937 !important;
+    }
+    
+    .stApp {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    }
+    
+    h1, h2, h3 {
+        color: #ffffff !important;
+        text-shadow: 0 2px 4px rgba(0,0,0,0.3);
+    }
+    
+    .stTextArea textarea {
+        background-color: #ffffff !important;
+        color: #1f2937 !important;
+        border: 2px solid #e5e7eb !important;
+        border-radius: 12px !important;
+        padding: 16px !important;
+        font-size: 16px !important;
+    }
+    
+    .stTextArea label {
+        color: #ffffff !important;
+        font-weight: 700 !important;
+    }
+    
+    .main-card {
+        background: linear-gradient(145deg, #ffffff 0%, #f8fafc 100%);
+        padding: 35px;
+        border-radius: 20px;
+        box-shadow: 0 20px 40px rgba(0,0,0,0.15);
+        margin-bottom: 25px;
+        text-align: center;
+        border: 1px solid rgba(255,255,255,0.3);
+        backdrop-filter: blur(10px);
+    }
+    
+    .result-title {
+        color: #475569 !important;
+        font-size: 16px;
+        text-transform: uppercase;
+        letter-spacing: 1.5px;
+        margin-bottom: 12px;
+        font-weight: 700;
+    }
+    
+    .result-value {
+        font-size: 52px;
+        font-weight: 800;
+        margin: 0;
+        text-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    
+    .section-header {
+        font-size: 22px;
+        font-weight: 700;
+        color: #1e293b !important;
+        margin-bottom: 20px;
+        border-left: 6px solid #3b82f6;
+        padding-left: 15px;
+        background: rgba(255,255,255,0.8);
+        padding: 12px 20px;
+        border-radius: 10px;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.1);
+    }
+    
+    .model-card {
+        background: linear-gradient(145deg, #ffffff 0%, #f1f5f9 100%);
+        padding: 25px;
+        border-radius: 16px;
+        box-shadow: 0 8px 25px rgba(0,0,0,0.12);
+        margin-bottom: 20px;
+        border: 1px solid rgba(255,255,255,0.5);
+        transition: all 0.3s ease;
+    }
+    
+    .model-card:hover {
+        transform: translateY(-5px);
+        box-shadow: 0 20px 40px rgba(0,0,0,0.2);
+    }
+    
+    .model-name {
+        color: #334155 !important;
+        font-size: 15px;
+        font-weight: 700;
+        margin-bottom: 12px;
+        border-bottom: 3px solid #e2e8f0;
+        padding-bottom: 8px;
+    }
+    
+    .prob-row {
+        margin-bottom: 18px;
+        background: rgba(255,255,255,0.9);
+        padding: 15px;
+        border-radius: 12px;
+        box-shadow: 0 2px 8px rgba(0,0,0,0.05);
+    }
+    
+    .prob-label {
+        font-size: 15px;
+        color: #1e293b !important;
+        font-weight: 700;
+        margin-bottom: 8px;
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+    }
+    
+    .prob-bar-bg {
+        width: 100%;
+        height: 14px;
+        background: linear-gradient(90deg, #f1f5f9, #e2e8f0);
+        border-radius: 7px;
+        overflow: hidden;
+        box-shadow: inset 0 2px 4px rgba(0,0,0,0.05);
+    }
+    
+    .prob-bar-fill {
+        height: 100%;
+        border-radius: 7px;
+        transition: width 0.8s ease;
+        box-shadow: 0 0 20px rgba(0,0,0,0.2);
+    }
+    
+    .stButton > button {
+        background: linear-gradient(45deg, #3b82f6, #1d4ed8) !important;
+        color: white !important;
+        border: none !important;
+        border-radius: 12px !important;
+        padding: 14px 28px !important;
+        font-weight: 700 !important;
+        font-size: 16px !important;
+        box-shadow: 0 8px 25px rgba(59,130,246,0.4) !important;
+        transition: all 0.3s ease !important;
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-2px) !important;
+        box-shadow: 0 12px 35px rgba(59,130,246,0.6) !important;
+    }
+    
+    .stRadio > div > label {
+        color: #ffffff !important;
+        font-weight: 600 !important;
+    }
+    
+    .stSelectbox > label {
+        color: #ffffff !important;
+        font-weight: 600 !important;
+    }
+    
+    .stExpander {
+        background: rgba(255,255,255,0.1) !important;
+        border-radius: 12px !important;
+        border: 1px solid rgba(255,255,255,0.2) !important;
+    }
 </style>
 """, unsafe_allow_html=True)
 
 id2label = {0: 'Very Negative', 1: 'Negative', 2: 'Neutral', 3: 'Positive', 4: 'Very Positive'}
-label_colors = {'Very Negative': '#DC2626', 'Negative': '#EA580C', 'Neutral': '#6B7280', 'Positive': '#16A34A', 'Very Positive': '#15803D'}
+label_colors = {
+    'Very Negative': '#ef4444', 
+    'Negative': '#f97316', 
+    'Neutral': '#64748b', 
+    'Positive': '#22c55e', 
+    'Very Positive': '#16a34a'
+}
 
 @st.cache_resource
 def load_models():
     models = {}
     
     standard_models = {
-        "BanglaBERT": "rocky250/political-banglabert",
-        "mBERT": "rocky250/political-mbert",
-        "B-Base": "rocky250/political-bbase",
-        "XLM-R": "rocky250/political-xlmr"
+        "BanglaBERT": "rocky250/Sentiment-banglabert",
+        "mBERT": "rocky250/Sentiment-mbert",
+        "B-Base": "rocky250/Sentiment-bbase",
+        "XLM-R": "rocky250/Sentiment-xlmr"
     }
     
     for name, repo in standard_models.items():
         try:
             tokenizer = AutoTokenizer.from_pretrained(repo)
             model = AutoModelForSequenceClassification.from_pretrained(repo)
-            models[name] = (tokenizer, model)
+            models[name] = (tokenizer, model.to('cuda' if torch.cuda.is_available() else 'cpu'))
         except:
-            st.warning(f"Could not load {name}")
+            continue
     
-  
     try:
-        creative_tokenizer = AutoTokenizer.from_pretrained("rocky250/bangla-political")
-        creative_model = CreativeBanglaPoliticalNet(num_classes=5)
-        creative_model.load_state_dict(torch.load("rocky250/bangla-political/pytorch_model.bin"))
-        models["Creative Hybrid"] = (creative_tokenizer, creative_model)
+        SA_tokenizer = AutoTokenizer.from_pretrained("rocky250/bangla-political")
+        model_SA = BanglaPoliticalNet(num_classes=5)
+        model_SA.load_state_dict(torch.load("rocky250/bangla-political/pytorch_model.bin", map_location='cpu'))
+        model_SA = model_SA.to('cuda' if torch.cuda.is_available() else 'cpu')
+        models["Creative Model"] = (SA_tokenizer, model_SA)
     except:
-        st.info("Bangla-political model not available - using standard models")
+        pass
     
     return models
 
@@ -105,13 +256,12 @@ def predict_single_model(text, model_name):
     clean_text = normalize(text)
     tokenizer, model = models_dict[model_name]
     
-    device = next(model.parameters()).device if hasattr(model, 'parameters') else torch.device('cpu')
-    
+    device = next(model.parameters()).device
     inputs = tokenizer(clean_text, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
     
     with torch.no_grad():
         if "Creative" in model_name:
-            logits = model(**inputs)
+            logits = model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
         else:
             outputs = model(**inputs)
             logits = outputs.logits
@@ -127,7 +277,7 @@ def predict_ensemble(text):
     all_probs = []
     all_predictions = []
     
-    for name in models_dict.keys():
+    for name in list(models_dict.keys())[:4]:
         try:
             pred, probs = predict_single_model(clean_text, name)
             all_probs.append(probs)
@@ -141,28 +291,39 @@ def predict_ensemble(text):
         return final_pred, all_predictions, avg_probs
     return "Error", [], np.zeros(5)
 
-st.markdown("<h1 style='text-align: center;'>Political Sentiment Analysis</h1>", unsafe_allow_html=True)
-st.markdown("<p style='text-align: center; color: #6B7280; font-size: 18px;'>Advanced Bengali Political Text Analysis with Custom Hybrid Architecture</p>", unsafe_allow_html=True)
+st.markdown("""
+<div style='
+    text-align: center; 
+    background: rgba(255,255,255,0.1); 
+    padding: 30px; 
+    border-radius: 20px; 
+    margin-bottom: 30px; 
+    backdrop-filter: blur(20px);
+'>
+    <h1 style='font-size: 3.5rem; margin: 0; background: linear-gradient(45deg, #ffffff, #e2e8f0); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-weight: 800;'>🇧🇩 Political Sentiment Analysis</h1>
+    <p style='font-size: 1.3rem; color: rgba(255,255,255,0.95); margin: 10px 0 0 0; font-weight: 500;'>Advanced Bengali Political Text Analysis with Custom Hybrid Architecture</p>
+</div>
+""", unsafe_allow_html=True)
 
-# Model selection
 col1, col2 = st.columns([3, 1])
 with col1:
-    user_input = st.text_area("", height=120, placeholder="এই বক্সে বাংলা রাজনৈতিক মন্তব্য লিখুন...", label_visibility="collapsed")
+    user_input = st.text_area("Enter Bengali political text:", height=140, 
+                            placeholder="এই বক্সে বাংলা রাজনৈতিক মন্তব্য লিখুন...", 
+                            help="Type or paste Bengali political text for sentiment analysis")
 
 with col2:
-    st.markdown("<div style='height: 40px'></div>", unsafe_allow_html=True)
+    st.markdown("<div style='height: 20px'></div>", unsafe_allow_html=True)
     mode = st.radio("Analysis Mode:", 
                    ["Single Model", "Ensemble (Recommended)"], 
-                   horizontal=True, 
-                   label_visibility="collapsed")
+                   horizontal=True)
     
     model_options = {f"({i+1}) {name}": name for i, name in enumerate(models_dict.keys())}
     selected_model = st.selectbox("Select Model:", list(model_options.keys()), index=0)
 
-analyze_btn = st.button("Analyze Sentiment", type="primary", use_container_width=True)
+analyze_btn = st.button("ANALYZE SENTIMENT", type="primary", use_container_width=True)
 
 if analyze_btn and user_input.strip():
-    with st.spinner('Processing with advanced models...'):
+    with st.spinner('Processing with advanced AI models...'):
         if mode == "Single Model":
             model_name = model_options[selected_model]
             final_res, probs = predict_single_model(user_input, model_name)
@@ -170,9 +331,10 @@ if analyze_btn and user_input.strip():
             col1, col2 = st.columns([1, 2])
             with col1:
                 st.markdown(f"""
-                <div class="main-card" style="border-top: 6px solid {label_colors[final_res]}">
+                <div class="main-card" style="border-top: 8px solid {label_colors[final_res]}">
                     <div class="result-title">{model_name}</div>
                     <div class="result-value" style="color: {label_colors[final_res]}">{final_res}</div>
+                    <div style="font-size: 18px; color: #64748b; margin-top: 15px;">Confidence: {max(probs)*100:.1f}%</div>
                 </div>
                 """, unsafe_allow_html=True)
             
@@ -182,30 +344,29 @@ if analyze_btn and user_input.strip():
                     label = id2label[i]
                     prob = probs[i] * 100
                     color = label_colors[label]
-                    opacity = "1.0" if prob > 5 else "0.4"
                     
                     st.markdown(f"""
-                    <div class="prob-row" style="opacity: {opacity}">
+                    <div class="prob-row">
                         <div class="prob-label">
-                            <span>{label}</span>
-                            <span>{prob:.1f}%</span>
+                            <span style="font-weight: 700;">{label}</span>
+                            <span style="font-weight: 700; color: {color};">{prob:.1f}%</span>
                         </div>
                         <div class="prob-bar-bg">
-                            <div class="prob-bar-fill" style="width: {min(prob, 100)}%; background-color: {color};"></div>
+                            <div class="prob-bar-fill" style="width: {min(prob, 100)}%; background: linear-gradient(90deg, {color}, {color}cc);"></div>
                         </div>
                     </div>
                     """, unsafe_allow_html=True)
         
-        else:  # Ensemble mode
+        else:
             final_res, all_votes, avg_probs = predict_ensemble(user_input)
             
-            main_col, details_col = st.columns([1, 1.5], gap="large")
+            main_col, details_col = st.columns([1, 1.4])
             
             with main_col:
                 st.markdown(f"""
-                <div class="main-card" style="border-top: 6px solid {label_colors[final_res]}">
-                    <div class="result-title">Ensemble Consensus</div>
-                    <div class="result-value" style="color: {label_colors[final_res]}">{final_res}</div>
+                <div class="main-card" style="border-top: 8px solid {label_colors[final_res]}; box-shadow: 0 25px 50px rgba(0,0,0,0.2);">
+                    <div class="result-title" style="font-size: 18px;">ENSEMBLE CONSENSUS</div>
+                    <div class="result-value" style="color: {label_colors[final_res]}; font-size: 60px;">{final_res}</div>
                 </div>
                 """, unsafe_allow_html=True)
                 
@@ -215,43 +376,45 @@ if analyze_btn and user_input.strip():
                     label = id2label[i]
                     prob = avg_probs[i] * 100
                     color = label_colors[label]
-                    opacity = "1.0" if prob > 1 else "0.3"
                     
                     st.markdown(f"""
-                    <div class="prob-row" style="opacity: {opacity}">
+                    <div class="prob-row">
                         <div class="prob-label">
                             <span>{label}</span>
-                            <span>{prob:.1f}%</span>
+                            <span style="color: {color};">{prob:.1f}%</span>
                         </div>
                         <div class="prob-bar-bg">
-                            <div class="prob-bar-fill" style="width: {min(prob, 100)}%; background-color: {color};"></div>
+                            <div class="prob-bar-fill" style="width: {min(prob, 100)}%; background: linear-gradient(90deg, {color}, {color}cc);"></div>
                         </div>
                     </div>
                     """, unsafe_allow_html=True)
             
             with details_col:
                 st.markdown('<div class="section-header">Individual Model Votes</div>', unsafe_allow_html=True)
-                cols = st.columns(2)
-                for idx, (name, vote) in enumerate(zip(models_dict.keys(), all_votes[:4])):
-                    with cols[idx % 2]:
+                model_cols = st.columns(2)
+                for idx, (name, vote) in enumerate(zip(list(models_dict.keys())[:4], all_votes[:4])):
+                    with model_cols[idx % 2]:
                         color = label_colors[vote]
                         st.markdown(f"""
                         <div class="model-card">
                             <div class="model-name">{name}</div>
-                            <div style="color: {color}; font-weight: 700; font-size: 20px;">{vote}</div>
+                            <div style="color: {color}; font-weight: 800; font-size: 24px; margin-top: 8px;">{vote}</div>
                         </div>
                         """, unsafe_allow_html=True)
 
 elif analyze_btn and not user_input.strip():
-    st.warning("অনুগ্রহ করে কিছু টেক্সট লিখুন!")
+    st.error("অনুগ্রহ করে কিছু টেক্সট লিখুন!")
 
-with st.expander("Example Political Texts"):
+with st.expander("Example Political Texts", expanded=False):
     examples = [
         "সরকারের এই নীতি দেশকে ধ্বংসের দিকে নিয়ে যাবে!",
         "চমৎকার সিদ্ধান্ত! দেশের জন্য গর্বিত। ভালো চলবে!",
         "রাজনীতির কোনো পরিবর্তন হবে না, সব একই রকম"
     ]
-    for example in examples:
-        if st.button(example, key=example[:20]):
-            st.session_state.user_input = example
-            st.rerun()
+    example_cols = st.columns(3)
+    for idx, example in enumerate(examples):
+        with example_cols[idx]:
+            if st.button(example[:40] + "..." if len(example) > 40 else example, 
+                        use_container_width=True):
+                st.session_state.user_input = example
+                st.rerun()