Upload 6 files

BERT_MODEL.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:47318e40a47b689c5d0cc90d41b345a4e3b0f15a2a4a01fd7916763fc5873e52
+size 266456825

TOKENIZER/special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

TOKENIZER/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

app.py

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+import streamlit as st
+import torch
+import torch.nn.functional as F
+from transformers import DistilBertTokenizer, DistilBertModel
+import time
+
+# Set page config with dark theme
+st.set_page_config(
+    page_title="TwittoBERT",
+    page_icon="🐦",
+    layout="centered",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for dark theme
+st.markdown("""
+    <style>
+        :root {
+            --primary-color: #1DA1F2;
+            --background-color: #0F0F0F;
+            --secondary-background: #1E1E1E;
+            --text-color: #FFFFFF;
+            --font: sans-serif;
+        }
+        
+        body {
+            background-color: var(--background-color);
+            color: var(--text-color);
+            font-family: var(--font);
+        }
+        
+        .stApp {
+            background-color: var(--background-color);
+        }
+        
+        .stTextInput>div>div>input {
+            background-color: var(--secondary-background);
+            color: var(--text-color);
+            border: 1px solid #333;
+        }
+        
+        .stButton>button {
+            background-color: var(--primary-color);
+            color: white;
+            border-radius: 8px;
+            padding: 0.5rem 1rem;
+            border: none;
+            font-weight: bold;
+            transition: all 0.3s;
+        }
+        
+        .stButton>button:hover {
+            background-color: #1991db;
+            transform: scale(1.02);
+        }
+        
+        .prediction-box {
+            padding: 1.5rem;
+            border-radius: 10px;
+            margin: 1.5rem 0;
+            background-color: var(--secondary-background);
+            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
+            border-left: 5px solid var(--primary-color);
+        }
+        
+        .header {
+            color: var(--primary-color);
+        }
+        
+        .positive {
+            border-left-color: #4CAF50;
+        }
+        
+        .neutral {
+            border-left-color: #FFCC00;
+        }
+        
+        .negative {
+            border-left-color: #FF4D4D;
+        }
+        
+        .sample-tweet {
+            padding: 0.5rem;
+            margin: 0.5rem 0;
+            border-radius: 5px;
+            background-color: var(--secondary-background);
+            cursor: pointer;
+            transition: all 0.2s;
+        }
+        
+        .sample-tweet:hover {
+            background-color: #2A2A2A;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# SentimentClassifier model definition
+class SentimentClassifier(torch.nn.Module):
+    def __init__(self):
+        super(SentimentClassifier, self).__init__()
+        self.bert = DistilBertModel.from_pretrained("distilbert-base-uncased")
+        for param in self.bert.parameters():
+            param.requires_grad = False
+        self.classifier = torch.nn.Sequential(
+            torch.nn.Linear(768, 256),
+            torch.nn.BatchNorm1d(256),
+            torch.nn.ReLU(),
+            torch.nn.Dropout(0.3),
+            torch.nn.Linear(256, 128),
+            torch.nn.BatchNorm1d(128),
+            torch.nn.ReLU(),
+            torch.nn.Dropout(0.3),
+            torch.nn.Linear(128, 64),
+            torch.nn.BatchNorm1d(64),
+            torch.nn.ReLU(),
+            torch.nn.Dropout(0.3),
+            torch.nn.Linear(64, 3)
+        )
+
+    def forward(self, input_ids, attention_mask):
+        bert_output = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        sentence_embeddings = bert_output.last_hidden_state[:, 0, :]
+        return self.classifier(sentence_embeddings)
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model():
+    model = SentimentClassifier()
+    model.load_state_dict(torch.load('BERT_MODEL.pth', map_location=torch.device('cpu')))
+    model.eval()
+    return model
+
+@st.cache_resource
+def load_tokenizer():
+    return DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+
+# Prediction function
+def predict_sentiment(model, tokenizer, tweet):
+    inputs = tokenizer(
+        tweet,
+        padding="max_length",
+        max_length=200,
+        truncation=True,
+        return_tensors="pt"
+    )
+    
+    input_ids = inputs["input_ids"]
+    attention_mask = inputs["attention_mask"]
+    
+    with torch.no_grad():
+        logits = model(input_ids, attention_mask)
+        probs = F.softmax(logits, dim=1)
+        confidence, predicted_class = torch.max(probs, dim=1)
+        
+        class_names = ["Negative", "Neutral", "Positive"]
+        label = class_names[predicted_class.item()]
+        confidence_percent = confidence.item() * 100
+    
+    return label, confidence_percent
+
+def main():
+    st.title("🐦 TwittoBERT")
+    st.markdown("Analyze the sentiment of tweets using a fine-tuned BERT model", unsafe_allow_html=True)
+    
+    # Load model and tokenizer
+    try:
+        model = load_model()
+        tokenizer = load_tokenizer()
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        st.stop()
+    
+    # Sample tweets
+    st.subheader("Try these sample tweets:")
+    sample_tweets = [
+        "I love this product! It's absolutely amazing! 😍",
+        "The service was okay, nothing special.",
+        "This is the worst experience I've ever had. Terrible!",
+        "Just had the best coffee of my life at this new café!",
+        "The movie was decent but could have been better.",
+        "I'm so frustrated with this terrible customer service!"
+    ]
+    
+    cols = st.columns(2)
+    for i, tweet in enumerate(sample_tweets):
+        with cols[i % 2]:
+            if st.button(tweet[:50] + "..." if len(tweet) > 50 else tweet, 
+                       key=f"sample_{i}",
+                       help="Click to analyze this tweet"):
+                st.session_state.sample_tweet = tweet
+    
+    # Tweet input
+    tweet = st.text_area("Or enter your own tweet to analyze:", 
+                        height=100, 
+                        placeholder="Type your tweet here...",
+                        value=st.session_state.get("sample_tweet", ""))
+    
+    if st.button("Analyze Sentiment") and tweet:
+        with st.spinner("Analyzing sentiment..."):
+            time.sleep(0.5)  # Simulate processing time
+            label, confidence = predict_sentiment(model, tokenizer, tweet)
+            
+            # Display result with appropriate styling
+            if label == "Negative":
+                st.markdown(f"""
+                    <div class="prediction-box negative">
+                        <h3>Sentiment: {label}</h3>
+                        <p>Confidence: {confidence:.2f}%</p>
+                    </div>
+                """, unsafe_allow_html=True)
+            elif label == "Neutral":
+                st.markdown(f"""
+                    <div class="prediction-box neutral">
+                        <h3>Sentiment: {label}</h3>
+                        <p>Confidence: {confidence:.2f}%</p>
+                    </div>
+                """, unsafe_allow_html=True)
+            else:
+                st.markdown(f"""
+                    <div class="prediction-box positive">
+                        <h3>Sentiment: {label}</h3>
+                        <p>Confidence: {confidence:.2f}%</p>
+                    </div>
+                """, unsafe_allow_html=True)
+
+    # Sidebar info
+    st.sidebar.header("About")
+    st.sidebar.markdown("""
+        This app uses a fine-tuned DistilBERT model to analyze sentiment in tweets. 
+        It can classify tweets as Positive, Negative, or Neutral with confidence scores.
+    """)
+    
+    st.sidebar.header("Model Info")
+    st.sidebar.text("Model: DistilBERT-base-uncased")
+    st.sidebar.text("Classes: Negative, Neutral, Positive")
+
+if __name__ == "__main__":
+    main()