Update app.py

app.py

@@ -1,6 +1,6 @@
 """
 HuggingFace Space App - News Sentiment Analyzer
-ZeroGPU Compatible Version
+Compatible with Gradio 6.20.0
 """
 
 import gradio as gr
@@ -37,6 +37,7 @@ print("✅ Siebert model loaded!")
 
 print("🎯 All models ready!")
 
+
 # ============================================
 # SENTIMENT ANALYSIS FUNCTIONS
 # ============================================
@@ -143,7 +144,7 @@ def get_ensemble_sentiment(text):
 
 
 def predict_single(text):
-    """Predict sentiment for single text"""
+    """Predict sentiment for single text - returns formatted string for Gradio"""
     if not text or len(text.strip()) < 5:
         return "⚠️ Please enter some text to analyze."
     
@@ -185,36 +186,100 @@ def predict_single(text):
 """
 
 
+# ============================================
+# API ENDPOINT FOR EXTERNAL CALLS
+# ============================================
+
+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
 # ============================================
 
-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="""
+# Create the interface with Gradio 6.20.0 syntax
+with gr.Blocks(title="📰 News Sentiment Analyzer", theme="soft") as demo:
+    gr.Markdown("""
+    # 📰 News Sentiment Analyzer
+    
     ### 🎯 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)
-    """,
-    examples=[
-        ["Apple reported record profits with revenue growth of 15% this quarter."],
-        ["The company faces severe criticism over data breach affecting millions of users."],
-        ["Government announced new policies to boost economic growth."],
-        ["Defense ministry successfully tested new hypersonic missile system."]
-    ],
-    theme="soft",
-    cache_examples=True
-)
+    
+    The ensemble approach provides more robust results by combining both models.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(
+                label="📝 Enter News Text",
+                placeholder="Paste your news article here...",
+                lines=10,
+                max_lines=20
+            )
+            
+            with gr.Row():
+                submit_btn = gr.Button("🔍 Analyze Sentiment", variant="primary")
+                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
+            
+            # Examples section
+            gr.Markdown("### 💡 Try these examples:")
+            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. Strategic capabilities enhanced.",
+                "The bank reported $2 billion in losses due to failed investments. CEO resigned amid controversy."
+            ]
+            
+            for i, example in enumerate(examples):
+                gr.Examples(
+                    inputs=text_input,
+                    examples=[[example]],
+                    label=f"Example {i+1}"
+                )
+        
+        with gr.Column(scale=1):
+            output = gr.Markdown(label="📈 Analysis Results", value="Enter text and click 'Analyze Sentiment' to see results here.")
+    
+    # Connect functions
+    submit_btn.click(
+        fn=predict_single,
+        inputs=text_input,
+        outputs=output
+    )
+    
+    clear_btn.click(
+        fn=lambda: "",
+        inputs=[],
+        outputs=text_input
+    )
+    
+    # Also allow Enter key to submit
+    text_input.submit(
+        fn=predict_single,
+        inputs=text_input,
+        outputs=output
+    )
+
 
 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)