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app.py

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+#!/usr/bin/env python3
+"""
+Hugging Face Space App for Financial Sentiment Analysis Ensemble
+"""
+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+from datetime import datetime
+import json
+
+class FinancialSentimentEnsemble:
+    def __init__(self):
+        self.models = {}
+        self.tokenizers = {}
+        self.model_names = [
+            "codealchemist01/financial-sentiment-distilbert",
+            "codealchemist01/financial-sentiment-bert-large", 
+            "codealchemist01/financial-sentiment-improved"
+        ]
+        self.labels = ["Bearish 📉", "Neutral ➡️", "Bullish 📈"]
+        self.load_models()
+    
+    def load_models(self):
+        """Load all models and tokenizers"""
+        print("🚀 Loading Financial Sentiment Analysis Ensemble...")
+        
+        for i, model_name in enumerate(self.model_names):
+            try:
+                print(f"📥 Loading {model_name}...")
+                self.tokenizers[i] = AutoTokenizer.from_pretrained(model_name)
+                self.models[i] = AutoModelForSequenceClassification.from_pretrained(model_name)
+                self.models[i].eval()
+                print(f"✅ {model_name} loaded successfully!")
+            except Exception as e:
+                print(f"❌ Error loading {model_name}: {e}")
+        
+        print(f"🎉 Ensemble ready with {len(self.models)} models!")
+    
+    def predict_single_model(self, text, model_idx):
+        """Predict sentiment using a single model"""
+        if model_idx not in self.models:
+            return None
+            
+        try:
+            inputs = self.tokenizers[model_idx](
+                text, 
+                return_tensors="pt", 
+                truncation=True, 
+                padding=True, 
+                max_length=512
+            )
+            
+            with torch.no_grad():
+                outputs = self.models[model_idx](**inputs)
+                probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+            
+            return probabilities[0].numpy()
+        except Exception as e:
+            print(f"Error in model {model_idx}: {e}")
+            return None
+    
+    def predict_ensemble(self, text):
+        """Predict sentiment using ensemble of all models"""
+        if not text.strip():
+            return "Please enter some text to analyze.", {}, {}
+        
+        individual_predictions = {}
+        all_probabilities = []
+        
+        # Get predictions from each model
+        for i, model_name in enumerate(self.model_names):
+            probs = self.predict_single_model(text, i)
+            if probs is not None:
+                all_probabilities.append(probs)
+                
+                # Individual model results
+                predicted_class = np.argmax(probs)
+                confidence = probs[predicted_class]
+                
+                model_short_name = model_name.split("/")[-1].replace("financial-sentiment-", "").title()
+                individual_predictions[f"{model_short_name}"] = {
+                    "Prediction": self.labels[predicted_class],
+                    "Confidence": f"{confidence:.1%}"
+                }
+        
+        if not all_probabilities:
+            return "Error: No models available for prediction.", {}, {}
+        
+        # Ensemble prediction (average probabilities)
+        ensemble_probs = np.mean(all_probabilities, axis=0)
+        ensemble_prediction = np.argmax(ensemble_probs)
+        ensemble_confidence = ensemble_probs[ensemble_prediction]
+        
+        # Create probability distribution for visualization
+        prob_dict = {}
+        for i, label in enumerate(self.labels):
+            prob_dict[label] = float(ensemble_probs[i])
+        
+        # Result summary
+        result_text = f"""
+## 🎯 Ensemble Prediction: **{self.labels[ensemble_prediction]}**
+**Confidence:** {ensemble_confidence:.1%}
+
+### 📊 Probability Distribution:
+- 📉 Bearish: {ensemble_probs[0]:.1%}
+- ➡️ Neutral: {ensemble_probs[1]:.1%}  
+- 📈 Bullish: {ensemble_probs[2]:.1%}
+
+### 🤖 Individual Model Results:
+"""
+        
+        for model_name, result in individual_predictions.items():
+            result_text += f"- **{model_name}**: {result['Prediction']} ({result['Confidence']})\n"
+        
+        return result_text, prob_dict, individual_predictions
+
+# Initialize the ensemble
+ensemble = FinancialSentimentEnsemble()
+
+def analyze_sentiment(text):
+    """Main function for Gradio interface"""
+    return ensemble.predict_ensemble(text)
+
+# Example texts for demonstration
+examples = [
+    "The stock market is showing strong bullish momentum with record highs across major indices.",
+    "Company earnings fell short of expectations, leading to a significant drop in share price.",
+    "The Federal Reserve maintained interest rates, keeping market conditions stable.",
+    "Tesla's innovative battery technology could revolutionize the automotive industry.",
+    "Rising inflation concerns are creating uncertainty in the financial markets.",
+    "The merger announcement sent both companies' stock prices soaring.",
+    "Quarterly results were mixed, with some sectors outperforming while others lagged."
+]
+
+# Create Gradio interface
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    title="Financial Sentiment Analysis Ensemble",
+    css="""
+    .gradio-container {
+        max-width: 1200px !important;
+    }
+    .main-header {
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .model-info {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        padding: 1rem;
+        border-radius: 10px;
+        margin: 1rem 0;
+    }
+    """
+) as demo:
+    
+    gr.HTML("""
+    <div class="main-header">
+        <h1>🚀 Financial Sentiment Analysis Ensemble</h1>
+        <p>Advanced AI-powered sentiment analysis for financial texts using an ensemble of 3 fine-tuned models</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(
+                label="📝 Enter Financial Text",
+                placeholder="Type or paste financial news, social media posts, or market commentary here...",
+                lines=4,
+                max_lines=10
+            )
+            
+            analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", size="lg")
+            
+            gr.Examples(
+                examples=examples,
+                inputs=text_input,
+                label="💡 Try these examples:"
+            )
+        
+        with gr.Column(scale=3):
+            result_output = gr.Markdown(label="📊 Analysis Results")
+            
+            with gr.Row():
+                prob_plot = gr.BarPlot(
+                    x="Sentiment",
+                    y="Probability", 
+                    title="Ensemble Probability Distribution",
+                    x_title="Sentiment Categories",
+                    y_title="Probability",
+                    width=400,
+                    height=300
+                )
+                
+                individual_results = gr.JSON(
+                    label="🤖 Individual Model Predictions",
+                    visible=True
+                )
+    
+    # Model Information
+    gr.HTML("""
+    <div class="model-info">
+        <h3>🧠 Ensemble Models:</h3>
+        <ul>
+            <li><strong>DistilBERT Model:</strong> Fast and efficient, optimized for real-time analysis</li>
+            <li><strong>BERT-Large Model:</strong> High accuracy with deep contextual understanding</li>
+            <li><strong>Improved Model:</strong> Enhanced with advanced training techniques</li>
+        </ul>
+        <p><strong>Ensemble Accuracy:</strong> 79.7% | <strong>Categories:</strong> Bearish 📉, Neutral ➡️, Bullish 📈</p>
+    </div>
+    """)
+    
+    # Event handlers
+    analyze_btn.click(
+        fn=analyze_sentiment,
+        inputs=text_input,
+        outputs=[result_output, prob_plot, individual_results]
+    )
+    
+    text_input.submit(
+        fn=analyze_sentiment,
+        inputs=text_input,
+        outputs=[result_output, prob_plot, individual_results]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )