Update app.py

app.py

@@ -1,75 +1,77 @@
-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)
+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}
+        return {"message": "Please Contact the owner to switch this space on."}
+    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}
+        return {"message": "Please Contact the owner to switch this space on."}
+    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)