app.py

import gradio as gr
from transformers import pipeline

# Load a crypto-specific sentiment model (e.g., ElKulako/cryptobert)
sentiment_pipeline = pipeline(
    "text-classification",
    model="ElKulako/cryptobert",  # Pre-trained on crypto data
    tokenizer="ElKulako/cryptobert"
)

def analyze(text):
    # Get the model's initial prediction
    result = sentiment_pipeline(text)[0]
    
    # Override logic for crypto-specific keywords (case-insensitive)
    text_lower = text.lower()
    
    # Force "positive" for bullish terms
    bullish_keywords = ["etf approved", "bullish", "halving", "burn", "greenlighted"]
    if any(keyword in text_lower for keyword in bullish_keywords):
        return {"label": "positive", "score": 0.99}
        
    # Force "negative" for bearish terms
    bearish_keywords = ["sec lawsuit", "hack", "fud", "sell-off", "delist"]
    if any(keyword in text_lower for keyword in bearish_keywords):
        return {"label": "negative", "score": 0.99}
    
    # Return original prediction if no keywords matched
    return {"label": result["label"], "score": result["score"]}

# Configure Gradio interface for API compatibility
app = gr.Interface(
    fn=analyze,
    inputs=gr.Textbox(placeholder="Enter crypto news headline..."),
    outputs=gr.JSON(),  # JSON output for n8n integration
    title="Crypto-Specific Sentiment Analysis",
    description="Analyzes sentiment of crypto news headlines. Overrides neutral predictions for key terms like 'ETF approved' or 'SEC lawsuit'.",
    flagging_mode="never"
)

# Launch Gradio app
app.launch(share=True)  # 'share' parameter will generate a public link