Update src/streamlit_app.py

src/streamlit_app.py

@@ -14,12 +14,10 @@ class BanglaPoliticalNet(nn.Module):
         super().__init__()
         self.banglabert = AutoModel.from_pretrained("csebuetnlp/banglabert")
         self.hidden_size = self.banglabert.config.hidden_size
-        
         self.cnn_layers = nn.ModuleList([
-            nn.Conv1d(self.hidden_size, 128, kernel_size=k, padding=k//2) 
+            nn.Conv1d(self.hidden_size, 128, kernel_size=k, padding=k//2)
             for k in [3,5,7]
         ])
-        
         self.attention = nn.MultiheadAttention(self.hidden_size, 8, batch_first=True)
         self.classifier = nn.Sequential(
             nn.Dropout(0.3),
@@ -28,256 +26,224 @@ class BanglaPoliticalNet(nn.Module):
             nn.Dropout(0.2),
             nn.Linear(512, num_classes)
         )
-        
+
     def forward(self, input_ids, attention_mask=None):
         bert_out = self.banglabert(input_ids, attention_mask=attention_mask).last_hidden_state
-        
         cnn_features = []
         for cnn in self.cnn_layers:
             cnn_out = cnn(bert_out.transpose(1,2)).transpose(1,2)
             cnn_features.append(F.relu(cnn_out))
-        
         cnn_concat = torch.cat(cnn_features, dim=-1)
         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)
         return logits
 
 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 !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;
-    }
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap');
+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': '#ef4444', 
-    'Negative': '#f97316', 
-    'Neutral': '#64748b', 
-    'Positive': '#22c55e', 
+    'Very Negative': '#ef4444',
+    'Negative': '#f97316',
+    'Neutral': '#64748b',
+    'Positive': '#22c55e',
     'Very Positive': '#16a34a'
 }
 
+models = {
+    "model_banglabert": "rocky250/Sentiment-banglabert",
+    "model_mbert": "rocky250/Sentiment-mbert",
+    "model_bbase": "rocky250/Sentiment-bbase",
+    "model_xlmr": "rocky250/Sentiment-xlmr",
+    "bangla_political": "rocky250/bangla-political"  
+}
+
 @st.cache_resource
 def load_models():
-    models = {}
-    
-    standard_models = {
-        "BanglaBERT": "rocky250/Sentiment-banglabert",
-        "mBERT": "rocky250/Sentiment-mbert",
-        "B-Base": "rocky250/Sentiment-bbase",
-        "XLM-R": "rocky250/Sentiment-xlmr",
-        "Bangla-P": "rocky250/bangla-political"
-    }
-    
-    for name, repo in standard_models.items():
+    models_dict = {}
+    for key, repo in models.items():
         try:
             tokenizer = AutoTokenizer.from_pretrained(repo)
             model = AutoModelForSequenceClassification.from_pretrained(repo)
-            models[name] = (tokenizer, model.to('cuda' if torch.cuda.is_available() else 'cpu'))
+            models_dict[key] = (tokenizer, model.to('cuda' if torch.cuda.is_available() else 'cpu'))
         except:
             continue
-    
-    try:
-        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:
-        pass
-    
-    return models
+        try:
+            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_dict["Creative Model"] = (SA_tokenizer, model_SA)
+        except:
+            pass
+    return models_dict
 
 models_dict = load_models()
 
 def predict_single_model(text, model_name):
     clean_text = normalize(text)
     tokenizer, model = models_dict[model_name]
-    
     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(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
         else:
             outputs = model(**inputs)
             logits = outputs.logits
-    
     probs = F.softmax(logits, dim=1).cpu().numpy()[0]
     pred_id = np.argmax(probs)
     prediction = id2label[pred_id]
-    
     return prediction, probs
 
 def predict_ensemble(text):
     clean_text = normalize(text)
     all_probs = []
     all_predictions = []
-    
     for name in list(models_dict.keys())[:4]:
         try:
             pred, probs = predict_single_model(clean_text, name)
@@ -285,7 +251,6 @@ def predict_ensemble(text):
             all_predictions.append(pred)
         except:
             continue
-    
     if all_probs:
         avg_probs = np.mean(all_probs, axis=0)
         final_pred = id2label[np.argmax(avg_probs)]
@@ -294,31 +259,34 @@ def predict_ensemble(text):
 
 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);
+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>
+<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>
 </div>
 """, unsafe_allow_html=True)
 
 col1, col2 = st.columns([3, 1])
 with col1:
-    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")
+    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: 20px'></div>", unsafe_allow_html=True)
-    mode = st.radio("Analysis Mode:", 
-                   ["Single Model", "Ensemble"], 
-                   horizontal=True)
-    
+    mode = st.radio("Analysis Mode:",
+                    ["Single Model", "Ensemble"],
+                    horizontal=True)
+
+if mode == "Single Model":
     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)
+else:
+    st.markdown("<div style='height: 50px'></div>", unsafe_allow_html=True)
 
 analyze_btn = st.button("ANALYZE SENTIMENT", type="primary", use_container_width=True)
 
@@ -327,68 +295,68 @@ if analyze_btn and user_input.strip():
         if mode == "Single Model":
             model_name = model_options[selected_model]
             final_res, probs = predict_single_model(user_input, model_name)
-            
+
             col1, col2 = st.columns([1, 2])
             with col1:
                 st.markdown(f"""
                 <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 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)
-            
+
             with col2:
                 st.markdown('<div class="section-header">Confidence Scores</div>', unsafe_allow_html=True)
                 for i in range(5):
                     label = id2label[i]
                     prob = probs[i] * 100
                     color = label_colors[label]
-                    
+
                     st.markdown(f"""
                     <div class="prob-row">
-                        <div class="prob-label">
-                            <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: linear-gradient(90deg, {color}, {color}cc);"></div>
-                        </div>
+                    <div class="prob-label">
+                    <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: linear-gradient(90deg, {color}, {color}cc);"></div>
+                    </div>
                     </div>
                     """, unsafe_allow_html=True)
-        
+
         else:
             final_res, all_votes, avg_probs = predict_ensemble(user_input)
-            
+
             main_col, details_col = st.columns([1, 1.4])
-            
+
             with main_col:
                 st.markdown(f"""
                 <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 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)
-                
+
                 st.markdown('<div class="section-header">Ensemble Probabilities</div>', unsafe_allow_html=True)
-                
+
                 for i in range(5):
                     label = id2label[i]
                     prob = avg_probs[i] * 100
                     color = label_colors[label]
-                    
+
                     st.markdown(f"""
                     <div class="prob-row">
-                        <div class="prob-label">
-                            <span>{label}</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: linear-gradient(90deg, {color}, {color}cc);"></div>
-                        </div>
+                    <div class="prob-label">
+                    <span>{label}</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: 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)
                 model_cols = st.columns(2)
@@ -397,8 +365,8 @@ if analyze_btn and user_input.strip():
                         color = label_colors[vote]
                         st.markdown(f"""
                         <div class="model-card">
-                            <div class="model-name">{name}</div>
-                            <div style="color: {color}; font-weight: 800; font-size: 24px; margin-top: 8px;">{vote}</div>
+                        <div class="model-name">{name}</div>
+                        <div style="color: {color}; font-weight: 800; font-size: 24px; margin-top: 8px;">{vote}</div>
                         </div>
                         """, unsafe_allow_html=True)
 
@@ -414,7 +382,7 @@ with st.expander("Example Political Texts", expanded=False):
     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, 
+            if st.button(example[:40] + "..." if len(example) > 40 else example,
                         use_container_width=True):
                 st.session_state.user_input = example
                 st.rerun()