Create app.py

app.py

@@ -0,0 +1,74 @@
+from fastapi import FastAPI
+import pickle
+from pydantic import BaseModel
+import streamlit as st
+
+app = FastAPI()
+
+pickle_in = open("classifier.pkl", "rb")
+classifier = pickle.load(pickle_in)
+
+
+class Classe(BaseModel):
+    Sepal_Length: float
+    Sepal_Width: float
+    Petal_Length: float
+    Petal_Width: float
+
+
+@app.get("/")
+def index():
+    return {"hello": "FastAPI"}
+
+
+@app.get('/{name}')
+def get_name(name: str):
+    return {'message': f'hello, {name}'}
+
+
+@app.post('/predict')
+def predict_species(data: Classe):
+    Sepal_Length = data.Sepal_Length
+    Sepal_Width = data.Sepal_Width
+    Petal_Length = data.Petal_Length
+    Petal_Width = data.Petal_Width
+
+    prediction = classifier.predict([[Sepal_Length, Sepal_Width, Petal_Length, Petal_Width]])
+
+    if prediction[0] == 0:
+        species = "setosa"
+    elif prediction[0] == 1:
+        species = "virginica"
+    elif prediction[0] == 2:
+        species = "versicolor"
+    else:
+        species = "unknown"
+
+    return {'prediction': species}
+
+
+if __name__ == "__main__":
+    import uvicorn
+    import subprocess
+
+    uvicorn_proc = subprocess.Popen(["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    st.title("Iris Species Prediction")
+    st.subheader("Enter the following parameters:")
+    sepal_length = st.number_input("Sepal Length", min_value=0.0, max_value=10.0, step=0.1, value=5.0)
+    sepal_width = st.number_input("Sepal Width", min_value=0.0, max_value=10.0, step=0.1, value=3.5)
+    petal_length = st.number_input("Petal Length", min_value=0.0, max_value=10.0, step=0.1, value=1.4)
+    petal_width = st.number_input("Petal Width", min_value=0.0, max_value=10.0, step=0.1, value=0.2)
+
+    submit = st.button("Predict")
+    if submit:
+        payload = {"Sepal_Length": sepal_length, "Sepal_Width": sepal_width, "Petal_Length": petal_length, "Petal_Width": petal_width}
+        prediction = st.empty()
+        with st.spinner("Predicting..."):
+            response = requests.post("http://localhost:8000/predict", json=payload)
+            if response.status_code == 200:
+                prediction_result = response.json()
+                prediction.success(f"Prediction: {prediction_result['prediction']}")
+            else:
+                prediction.error("Prediction failed.")
+
+    uvicorn_proc.kill()