app.py

#!/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
    )