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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import pickle
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import classification_report, accuracy_score
+from sklearn.model_selection import train_test_split
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from pydantic import BaseModel
+import io
+
+app = FastAPI()
+data = None
+
+# Function to train the model
+def train_aut(data):
+    data['Downtime_Flag'] = data['Downtime_Flag'].map({'Yes': 1, 'No': 0})
+    X = data[['Temperature', 'Run_Time']]
+    y = data['Downtime_Flag']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    with open('model.pkl', 'wb') as file:
+        pickle.dump(model, file)
+    y_pred = model.predict(X_test)
+    accuracy = accuracy_score(y_test, y_pred)
+    f1 = classification_report(y_test, y_pred, output_dict=True)['1']['f1-score']
+    return accuracy, f1
+
+# Function to make predictions
+def predict_aut(temp, run_time):
+    try:
+        with open('model.pkl', 'rb') as file:
+            model = pickle.load(file)
+        input_data = [[temp, run_time]]
+        y_pred = model.predict(input_data)
+        return 'Yes' if y_pred[0] == 1 else 'No'
+    except FileNotFoundError:
+        raise HTTPException(status_code=400, detail="Model not trained. Please upload data and train the model first.")
+
+# Pydantic model for prediction input
+class PredictionInput(BaseModel):
+    Temperature: float
+    Run_Time: float
+
+@app.post("/upload")
+async def upload(file: UploadFile = File(...)):
+    try:
+        global data
+        contents = await file.read()
+        data = pd.read_csv(io.StringIO(contents.decode("utf-8")))
+        return {"message": "File uploaded successfully."}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Error reading file: {str(e)}")
+
+@app.post("/train")
+def train():
+    global data
+    if data is None:
+        raise HTTPException(status_code=400, detail="No data uploaded. Please upload a dataset first.")
+    try:
+        accuracy, f1 = train_aut(data)
+        return {"message": "Model trained successfully.", "accuracy": accuracy, "f1_score": f1}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error during training: {str(e)}")
+
+@app.post("/predict")
+def predict(input_data: PredictionInput):
+    try:
+        result = predict_aut(input_data.Temperature, input_data.Run_Time)
+        return {"Downtime": result}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error during prediction: {str(e)}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+annotated-types==0.7.0
+anyio==4.8.0
+certifi==2024.12.14
+click==8.1.8
+colorama==0.4.6
+dnspython==2.7.0
+email_validator==2.2.0
+exceptiongroup==1.2.2
+fastapi==0.115.6
+fastapi-cli==0.0.7
+h11==0.14.0
+httpcore==1.0.7
+httptools==0.6.4
+httpx==0.28.1
+idna==3.10
+Jinja2==3.1.5
+joblib==1.4.2
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+mdurl==0.1.2
+numpy==2.2.2
+pandas==2.2.3
+pydantic==2.10.5
+pydantic_core==2.27.2
+Pygments==2.19.1
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.20
+pytz==2024.2
+PyYAML==6.0.2
+rich==13.9.4
+rich-toolkit==0.13.2
+scikit-learn==1.6.1
+scipy==1.15.1
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+starlette==0.41.3
+threadpoolctl==3.5.0
+typer==0.15.1
+typing_extensions==4.12.2
+tzdata==2024.2
+uvicorn==0.34.0
+watchfiles==1.0.4
+websockets==14.2