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app.py

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+"""
+HuggingFace Space App - News Sentiment Analyzer
+ZeroGPU Compatible Version - torch 2.8.0
+"""
+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from scipy.special import softmax
+import numpy as np
+
+print("🚀 Loading Sentiment Models...")
+
+# Device configuration - ZeroGPU compatible
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"📱 Using device: {device}")
+
+# ============================================
+# 1. LOAD ROBERTA MODEL
+# ============================================
+print("📥 Loading RoBERTa model...")
+roberta_model_name = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+roberta_tokenizer = AutoTokenizer.from_pretrained(roberta_model_name)
+roberta_model = AutoModelForSequenceClassification.from_pretrained(roberta_model_name).to(device)
+roberta_model.eval()
+print("✅ RoBERTa model loaded!")
+
+# ============================================
+# 2. LOAD SIEBERT MODEL
+# ============================================
+print("📥 Loading Siebert model...")
+siebert_model_name = "siebert/sentiment-roberta-large-english"
+siebert_tokenizer = AutoTokenizer.from_pretrained(siebert_model_name)
+siebert_model = AutoModelForSequenceClassification.from_pretrained(siebert_model_name).to(device)
+siebert_model.eval()
+print("✅ Siebert model loaded!")
+
+print("🎯 All models ready!")
+
+
+# ============================================
+# SENTIMENT ANALYSIS FUNCTIONS
+# ============================================
+
+def analyze_roberta(text):
+    """Analyze sentiment using RoBERTa model"""
+    try:
+        if not text or len(text.strip()) < 5:
+            return {'negative': 0.33, 'neutral': 0.34, 'positive': 0.33}
+        
+        encoded = roberta_tokenizer(
+            text, 
+            return_tensors='pt', 
+            truncation=True, 
+            max_length=512,
+            padding=True
+        ).to(device)
+        
+        with torch.no_grad():
+            output = roberta_model(**encoded)
+        
+        scores = softmax(output.logits.cpu().numpy()[0])
+        return {
+            'negative': float(scores[0]),
+            'neutral': float(scores[1]),
+            'positive': float(scores[2])
+        }
+    except Exception as e:
+        print(f"❌ RoBERTa error: {e}")
+        return {'negative': 0.33, 'neutral': 0.34, 'positive': 0.33}
+
+
+def analyze_siebert(text):
+    """Analyze sentiment using Siebert model"""
+    try:
+        if not text or len(text.strip()) < 5:
+            return {'negative': 0.5, 'positive': 0.5}
+        
+        encoded = siebert_tokenizer(
+            text, 
+            return_tensors='pt', 
+            truncation=True, 
+            max_length=512,
+            padding=True
+        ).to(device)
+        
+        with torch.no_grad():
+            output = siebert_model(**encoded)
+        
+        scores = softmax(output.logits.cpu().numpy()[0])
+        return {
+            'negative': float(scores[0]),
+            'positive': float(scores[1])
+        }
+    except Exception as e:
+        print(f"❌ Siebert error: {e}")
+        return {'negative': 0.5, 'positive': 0.5}
+
+
+def get_ensemble_sentiment(text):
+    """Combine both models for accurate results"""
+    
+    roberta_result = analyze_roberta(text)
+    siebert_result = analyze_siebert(text)
+    
+    siebert_neg = siebert_result['negative']
+    siebert_pos = siebert_result['positive']
+    diff = abs(siebert_pos - siebert_neg)
+    
+    if diff < 0.2:
+        siebert_neutral = 1 - diff
+        siebert_neg = siebert_neg * (1 - siebert_neutral/2)
+        siebert_pos = siebert_pos * (1 - siebert_neutral/2)
+    else:
+        siebert_neutral = 0.1
+        siebert_neg = siebert_neg * 0.95
+        siebert_pos = siebert_pos * 0.95
+    
+    ensemble_neg = (roberta_result['negative'] + siebert_neg) / 2
+    ensemble_neu = (roberta_result['neutral'] + siebert_neutral) / 2
+    ensemble_pos = (roberta_result['positive'] + siebert_pos) / 2
+    
+    total = ensemble_neg + ensemble_neu + ensemble_pos
+    if total > 0:
+        ensemble_neg /= total
+        ensemble_neu /= total
+        ensemble_pos /= total
+    
+    max_score = max(ensemble_neg, ensemble_neu, ensemble_pos)
+    if max_score == ensemble_neg:
+        label = 'Negative'
+    elif max_score == ensemble_pos:
+        label = 'Positive'
+    else:
+        label = 'Neutral'
+    
+    return {
+        'sentiment': label,
+        'confidence': round(max_score, 4),
+        'negative_score': round(ensemble_neg, 4),
+        'neutral_score': round(ensemble_neu, 4),
+        'positive_score': round(ensemble_pos, 4)
+    }
+
+
+def predict_single(text):
+    """Predict sentiment for single text"""
+    if not text or len(text.strip()) < 5:
+        return "⚠️ Please enter some text to analyze."
+    
+    result = get_ensemble_sentiment(text)
+    
+    sentiment_emoji = {'Positive': '✅', 'Negative': '❌', 'Neutral': '⚪'}
+    emoji = sentiment_emoji.get(result['sentiment'], '⚪')
+    confidence_pct = result['confidence'] * 100
+    
+    if confidence_pct > 80:
+        confidence_color = "🟢"
+    elif confidence_pct > 60:
+        confidence_color = "🟡"
+    else:
+        confidence_color = "🔴"
+    
+    return f"""
+# 📊 Sentiment Analysis Results
+
+---
+
+### {emoji} **Final Sentiment: {result['sentiment']}**
+**Confidence:** {confidence_color} {confidence_pct:.1f}%
+
+---
+
+### 📈 Score Breakdown
+
+| Aspect | Score |
+|--------|-------|
+| ✅ Positive | {result['positive_score']*100:.1f}% |
+| ❌ Negative | {result['negative_score']*100:.1f}% |
+| ⚪ Neutral | {result['neutral_score']*100:.1f}% |
+
+---
+
+### 📝 Analyzed Text
+> {text[:200]}{'...' if len(text) > 200 else ''}
+"""
+
+
+def api_predict(text):
+    """API endpoint for external calls (JSON response)"""
+    if not text or len(text.strip()) < 5:
+        return {
+            'error': 'Text too short or empty',
+            'sentiment': 'Neutral',
+            'confidence': 0.0
+        }
+    
+    return get_ensemble_sentiment(text)
+
+
+# ============================================
+# GRADIO INTERFACE
+# ============================================
+
+# Create the interface
+demo = gr.Interface(
+    fn=predict_single,
+    inputs=gr.Textbox(
+        label="📝 Enter News Text",
+        placeholder="Paste your news article here...",
+        lines=8,
+        max_lines=20
+    ),
+    outputs=gr.Markdown(label="📈 Analysis Results"),
+    title="📰 News Sentiment Analyzer",
+    description="""
+    ### 🎯 Ensemble Model: RoBERTa + Siebert
+    
+    This analyzer uses two powerful models combined for accurate sentiment analysis:
+    - **RoBERTa**: Twitter-based sentiment model (negative, neutral, positive)
+    - **Siebert**: Large-scale sentiment model (negative, positive)
+    
+    The ensemble approach provides more robust results by combining both models.
+    """,
+    examples=[
+        ["Apple reported record profits with revenue growth of 15% this quarter. The tech giant exceeded market expectations."],
+        ["The company faces severe criticism over data breach affecting millions of users. Stocks fell sharply."],
+        ["Government announced new policies to boost economic growth and create jobs. Markets reacted positively."],
+        ["Defense ministry successfully tested new hypersonic missile system."],
+        ["The bank reported $2 billion in losses due to failed investments. CEO resigned."]
+    ],
+    theme="soft",
+    cache_examples=True
+)
+
+if __name__ == "__main__":
+    print("\n" + "="*60)
+    print("🚀 News Sentiment Analyzer")
+    print("="*60)
+    print("📱 Device:", device)
+    print("🤖 Models: RoBERTa + Siebert")
+    print("="*60 + "\n")
+    
+    demo.launch(share=True)

requirements.txt

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+torch==2.8.0
+transformers==4.46.3
+scipy==1.15.2
+numpy==2.0.0
+gradio==5.16.0
+sentencepiece==0.2.0
+protobuf==5.29.3

space.py

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+"""
+Main entry point for HuggingFace Space
+"""
+
+from app import demo
+
+if __name__ == "__main__":
+    demo.launch()