Create app.py

app.py

@@ -0,0 +1,29 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import numpy as np
+
+app = FastAPI()
+
+# Load model
+model = joblib.load('BEST_MODEL_LightGBM_TFIDF.joblib')
+
+class PredictionRequest(BaseModel):
+    text: str
+    top_k: int = 3
+
+@app.post("/predict")
+def predict(request: PredictionRequest):
+    # Your prediction logic here
+    probabilities = model.predict_proba([request.text])[0]
+    top_indices = np.argsort(probabilities)[::-1][:request.top_k]
+    
+    results = []
+    for idx in top_indices:
+        results.append({
+            'sdg_number': idx + 1,
+            'sdg_name': f'SDG {idx + 1}',
+            'confidence': float(probabilities[idx])
+        })
+    
+    return {'predictions': results}