Create app.py

app.py

@@ -0,0 +1,67 @@
+# ============================================
+# FILE: app.py (main application file)
+# ============================================
+APP_PY = '''
+import gradio as gr
+from transformers import pipeline
+import json
+
+# Load your model
+try:
+    classifier = pipeline(
+        "text-classification",
+        model="archich/hate-speech-detector",
+        tokenizer="archich/hate-speech-detector"
+    )
+    print("✅ Model loaded successfully!")
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+    classifier = None
+
+def predict_hate_speech(text):
+    """Predict if text contains hate speech"""
+    if not text or not text.strip():
+        return {"error": "Please provide text to analyze"}
+    
+    try:
+        # Get predictions
+        results = classifier(text)
+        
+        # Format response
+        response = {
+            "input": text,
+            "predictions": results,
+            "is_hate_speech": results[0]["label"] in ["LABEL_1", "hate_speech", "HATE"],
+            "confidence": results[0]["score"]
+        }
+        
+        return json.dumps(response, indent=2)
+    
+    except Exception as e:
+        return {"error": str(e)}
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict_hate_speech,
+    inputs=gr.Textbox(
+        lines=3,
+        placeholder="Enter text to analyze...",
+        label="Input Text"
+    ),
+    outputs=gr.JSON(label="Analysis Result"),
+    title="🛡️ Hate Speech Detector API",
+    description="""
+    Analyze text for hate speech using the archich/hate-speech-detector model.
+    
+    **API Endpoint:** Use the API tab above or call this Space via API.
+    """,
+    examples=[
+        ["I love this community! Everyone is so kind."],
+        ["You are terrible and I hate you."],
+        ["This is a neutral statement about technology."]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()
+'''