komitdua

README.md

@@ -1,10 +1,111 @@
+# 🌸 Iris Classifier API 🌸
+
+A simple Flask API that predicts the species of iris flowers using a pre-trained Scikit-Learn model.
+This app is deployed on [Hugging Face Spaces](https://huggingface.co/spaces/vnsd13/iris) and served via Docker using Gunicorn.
+
+---
+
+## 🔍 What is this?
+
+This API receives 4 numeric features from the Iris dataset:
+
+* Sepal Length
+* Sepal Width
+* Petal Length
+* Petal Width
+
+And returns the predicted iris species using a `DecisionTreeClassifier`.
+
+---
+
+## 📆 Model
+
+* Trained with `scikit-learn==1.6.1`
+* Saved with `joblib`
+* Stored in `models/model.pkl`
+
+---
+
+## 🚀 How to Use
+
+### 🪪 API Endpoints
+
+#### `GET /`
+
+Returns basic info to confirm that the app is running.
+
+**Response:**
+
+```
+Iris Classifier API is running!
+```
+
+---
+
+#### `POST /predict`
+
+Predict the iris flower class based on features.
+
+**Request Body (JSON):**
+
+```json
+{
+  "sepal_length": 5.1,
+  "sepal_width": 3.5,
+  "petal_length": 1.4,
+  "petal_width": 0.2
+}
+```
+
+**Response:**
+
+```json
+{
+  "prediction": "setosa"
+}
+```
+
 ---
-title: Iris
-emoji: 💻
-colorFrom: red
-colorTo: gray
-sdk: docker
-pinned: false
+
+## 🐳 Docker Support
+
+App is built using the following `Dockerfile`:
+
+```dockerfile
+FROM python:3.10
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app"]
+```
+
+---
+
+## 📚 Requirements
+
+```
+flask
+numpy
+scikit-learn==1.6.1
+joblib
+gunicorn
+```
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 📱 Deployment
+
+This API is currently deployed to Hugging Face Spaces using Docker interface.
+
+URL: [https://vnsd13-iris.hf.space](https://vnsd13-iris.hf.space)
+
+---
+
+## 👨‍💻 Author
+
+Made by [@vnsd13](https://huggingface.co/vnsd13)

app.py

@@ -21,9 +21,6 @@ def predict():
             float(data["petal_width"]),
         ]
         prediction = model.predict([features])
-        return jsonify({"prediction": prediction[0]})
+        return jsonify({"prediction": int(prediction[0])})  # <-- Fix disini
     except Exception as e:
         return jsonify({"error": str(e)}), 400
-
-# NOTE: Don't run app here if you're using Gunicorn
-# Gunicorn will handle the server initialization