Add application file

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /code
+
+COPY ./app /code/app
+
+RUN pip install --upgrade pip
+RUN pip install -r app/requirements.txt
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

app/main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import pickle
+import numpy as np
+
+app = FastAPI()
+
+# Load model at startup
+with open("app/model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Input schema
+class InputData(BaseModel):
+    features: list[float]
+
+@app.get("/")
+def home():
+    return {"message": "Logistic Regression API is up!"}
+
+@app.post("/predict")
+def predict(data: InputData):
+    prediction = model.predict([np.array(data.features)])
+    return {"prediction": int(prediction[0])}

app/model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:45b08c2fb38af8d0d1a364923427ba12b8751ff44039eea2b4068dcf7f7579b0
+size 953

app/requirements.txt

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+fastapi
+uvicorn
+scikit-learn
+pandas

app/train.py

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+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+import pickle
+
+# Load data
+data = load_iris()
+X = pd.DataFrame(data.data, columns=data.feature_names)
+y = data.target
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Train model
+model = LogisticRegression(max_iter=200)
+model.fit(X_train, y_train)
+
+# Save model
+with open("app/model.pkl", "wb") as f:
+    pickle.dump(model, f)