Update app.py

app.py

@@ -1,22 +1,40 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the crypto-specific sentiment model
+# Load a crypto-specific sentiment model (e.g., ElKulako/cryptobert)
 sentiment_pipeline = pipeline(
     "text-classification",
-    model="ElKulako/cryptobert",  # Crypto-optimized model
+    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
 gr.Interface(
     fn=analyze,
     inputs=gr.Textbox(placeholder="Enter crypto news headline..."),
-    outputs=gr.JSON(),
+    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(
     server_name="0.0.0.0",