Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Set page config
+st.set_page_config(
+    page_title="Tweet Classifier",
+    page_icon="🐦",
+    layout="wide"
+)
+
+# Custom CSS for better styling
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 3rem;
+        color: #1DA1F2;
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .result-box {
+        background-color: #f0f2f6;
+        padding: 2rem;
+        border-radius: 10px;
+        margin-top: 2rem;
+    }
+    .confidence-bar {
+        height: 20px;
+        background: linear-gradient(90deg, #ff4b4b 0%, #ffa500 50%, #00cc00 100%);
+        border-radius: 10px;
+        margin: 10px 0;
+    }
+    .label-badge {
+        padding: 0.5rem 1rem;
+        border-radius: 20px;
+        font-weight: bold;
+        margin: 0.2rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# App title
+st.markdown('<h1 class="main-header">🐦 Tweet Sentiment Classifier</h1>', unsafe_allow_html=True)
+st.markdown("### Powered by your fine-tuned DistilBERT model (96.4% accuracy)")
+
+# Initialize model (with caching)
+@st.cache_resource
+def load_model():
+    try:
+        # Load your fine-tuned model
+        model_name = "ChatBotsTA/distilbert-tweet-classifier"
+        classifier = pipeline(
+            "text-classification",
+            model=model_name,
+            tokenizer=model_name,
+            device=0 if torch.cuda.is_available() else -1
+        )
+        return classifier
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+# Load model
+with st.spinner("🚀 Loading your fine-tuned model from Hugging Face..."):
+    classifier = load_model()
+
+if classifier is None:
+    st.error("Could not load the model. Please check if the model exists on Hugging Face.")
+    st.stop()
+
+# Label colors
+label_colors = {
+    "positive": "🟢",
+    "negative": "🔴", 
+    "litigious": "🔵",
+    "uncertainty": "🟡"
+}
+
+label_descriptions = {
+    "positive": "Positive sentiment/content",
+    "negative": "Negative sentiment",
+    "litigious": "Legal/contractual content", 
+    "uncertainty": "Uncertain/ambiguous content"
+}
+
+# Input section
+st.markdown("---")
+st.markdown("## 📝 Enter Tweet Text to Analyze")
+
+input_text = st.text_area(
+    "Paste tweet text here:",
+    height=150,
+    placeholder="Enter text to classify (e.g., 'This product is amazing!', 'I hate this service', 'The court case was dismissed')"
+)
+
+# Examples
+with st.expander("💡 Click for example texts"):
+    st.write("**Examples to try:**")
+    examples = [
+        "This is an amazing product! I love it!",
+        "I'm so frustrated with this service, terrible experience",
+        "The court case was dismissed due to lack of evidence",
+        "I'm not sure how I feel about this situation",
+        "This company's financial results exceeded all expectations",
+        "I might consider this option, but I need more information"
+    ]
+    for example in examples:
+        if st.button(example, key=example):
+            input_text = example
+
+# Analyze button
+if st.button("🔍 Analyze Tweet", type="primary", use_container_width=True):
+    if input_text.strip():
+        with st.spinner("Analyzing..."):
+            try:
+                # Get prediction
+                result = classifier(input_text)[0]
+                label = result['label']
+                confidence = result['score']
+                
+                # Display results
+                st.markdown("---")
+                st.markdown("## 📊 Analysis Results")
+                
+                # Result box
+                st.markdown(f'<div class="result-box">', unsafe_allow_html=True)
+                
+                # Label and confidence
+                col1, col2 = st.columns([1, 2])
+                with col1:
+                    st.markdown(f"### {label_colors.get(label, '⚪')} **Prediction:**")
+                    st.markdown(f'<span class="label-badge" style="background-color: {{
+                        "positive": "#4CAF50",
+                        "negative": "#F44336", 
+                        "litigious": "#2196F3",
+                        "uncertainty": "#FFC107"
+                    }.get(label, "#9E9E9E")}}; color: white;">{label.upper()}</span>', unsafe_allow_html=True)
+                    
+                with col2:
+                    st.markdown(f"### 📈 **Confidence:** {confidence:.1%}")
+                    # Confidence bar
+                    st.markdown(f'<div class="confidence-bar" style="width: {confidence*100}%;"></div>', unsafe_allow_html=True)
+                
+                # Description
+                st.markdown(f"**Description:** {label_descriptions.get(label, '')}")
+                
+                st.markdown('</div>', unsafe_allow_html=True)
+                
+                # Raw scores (optional)
+                with st.expander("🔍 View detailed scores"):
+                    # Get all label scores
+                    tokenizer = AutoTokenizer.from_pretrained("ChatBotsTA/distilbert-tweet-classifier")
+                    model = AutoModelForSequenceClassification.from_pretrained("ChatBotsTA/distilbert-tweet-classifier")
+                    
+                    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+                    with torch.no_grad():
+                        outputs = model(**inputs)
+                        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+                    
+                    scores = {model.config.id2label[i]: float(prob) 
+                             for i, prob in enumerate(probabilities[0])}
+                    
+                    for lbl, score in sorted(scores.items(), key=lambda x: x[1], reverse=True):
+                        st.write(f"{label_colors.get(lbl, '⚪')} {lbl}: {score:.3f}")
+                        
+            except Exception as e:
+                st.error(f"Error during prediction: {e}")
+    else:
+        st.warning("Please enter some text to analyze!")
+
+# Model info section
+st.markdown("---")
+st.markdown("## ℹ️ About This Model")
+
+st.info("""
+**Model Details:**
+- **Base Model**: DistilBERT-base-uncased
+- **Training**: Fine-tuned on 50,000 tweets
+- **Accuracy**: 96.4% on validation set
+- **Labels**: Positive, Negative, Litigious, Uncertainty
+- **Created By**: You! 🎯
+
+**How to use programmatically:**
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification", 
+                     model="ChatBotsTA/distilbert-tweet-classifier")
+result = classifier("Your text here")