Add application file

app.py

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+import nltk
+from fastapi import FastAPI, Form
+from fastapi.responses import PlainTextResponse
+import joblib
+
+# Download necessary NLTK resources
+nltk.download('wordnet', quiet=True)
+nltk.download('stopwords', quiet=True)
+
+# Load the trained model
+model = joblib.load('disaster_classification_model.joblib')
+
+app = FastAPI()
+
+@app.post("/predict", response_class=PlainTextResponse)
+async def predict(text: str = Form(...)):
+    # The preprocessing is now handled by the loaded pipeline
+    prediction = model.predict([text])[0]
+    return "disaster" if prediction == 1 else "not"
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Disaster Classification API"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+pandas
+scikit-learn
+nltk
+fastapi
+uvicorn
+joblib
+python-multipart