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import pickle
from flask import Flask, request, jsonify
from flask_cors import CORS
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
app = Flask(__name__)
CORS(app)
# --- Train or load model
try:
model = pickle.load(open("model.pkl", "rb"))
except:
iris = load_iris()
X, y = iris.data, iris.target
model = RandomForestClassifier()
model.fit(X, y)
pickle.dump(model, open("model.pkl", "wb"))
# --- Home route ---
@app.route("/", methods=["GET"])
def home():
return """
<h1>🌸 Iris Flower Classifier API</h1>
<p>This is a Flask-based backend hosted on Hugging Face Spaces.</p>
<p>It predicts the species of an Iris flower (<b>setosa</b>, <b>versicolor</b>, <b>virginica</b>)
based on sepal and petal measurements.</p>
<p><b>Frontend UI:</b>
<a href="https://nielitropar.github.io/iris/" target="_blank">
https://nielitropar.github.io/iris/
</a></p>
<p><b>API Endpoint:</b> <code>POST /predict</code></p>
<p>Example JSON body:</p>
<pre>{
"sepal_length": 5.1,
"sepal_width": 3.5,
"petal_length": 1.4,
"petal_width": 0.2
}</pre>
<p>Response:</p>
<pre>{
"prediction": "setosa",
"confidence": 0.98,
"probabilities": {
"setosa": 0.98,
"versicolor": 0.01,
"virginica": 0.01
}
}</pre>
"""
# --- Predict route ---
@app.route("/predict", methods=["POST"])
def predict():
data = request.json
features = [
data["sepal_length"],
data["sepal_width"],
data["petal_length"],
data["petal_width"]
]
# Predict class and probabilities
prediction_idx = model.predict([features])[0]
probs = model.predict_proba([features])[0]
target_names = load_iris().target_names
prediction_label = target_names[int(prediction_idx)]
# Build probability dict
probabilities = {
target_names[i]: float(probs[i])
for i in range(len(target_names))
}
confidence = float(max(probs))
return jsonify({
"prediction": prediction_label,
"confidence": confidence,
"probabilities": probabilities
})
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
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