Upload 3 files

main.py

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+from fastapi import FastAPI
+import pickle, uvicorn, os
+from pydantic import BaseModel
+import pandas as pd
+import numpy as np
+from sklearn import preprocessing
+from sklearn.impute import SimpleImputer
+from sklearn.compose import ColumnTransformer
+from sklearn.compose import make_column_selector as selector
+from sklearn.metrics import accuracy_score
+
+# Config & Setup
+## Variables of environment
+DIRPATH = os.path.dirname(__file__)
+ASSETSDIRPATH = os.path.join(DIRPATH, "asset")
+ml_component_pkl = os.path.join(ASSETSDIRPATH, "ml_component.pkl")
+
+print(
+    f" {'*'*10} Config {'*'*10}\n INFO: DIRPATH = {DIRPATH} \n INFO: ASSETSDIRPATH = {ASSETSDIRPATH} "
+)
+
+
+# API Basic config
+app = FastAPI(
+    title="Titanic Survivors API",
+    version="0.0.1",
+    description="Prediction of Titanic Survivors",
+)
+
+## Loading of assets
+with open(ml_component_pkl, "rb") as f:
+    loaded_items = pickle.load(f)
+#print("INFO:    Loaded assets:", loaded_items)
+
+pipeline_of_my_model = loaded_items["pipeline"]
+num_cols = loaded_items['numeric_columns']
+cat_cols = loaded_items['categorical_columns']
+
+## BaseModel
+class ModelInput(BaseModel):
+    PeopleInTicket: int
+    Age: float
+    FarePerPerson: float
+    SibSp: int
+    Pclass: int
+    Fare: float
+    Parch: int
+    TicketNumber: float
+    Embarked: str
+    Sex: str
+
+## Utils
+# def processing_FE(
+#     dataset, scaler, encoder,imputer, FE=pipeline_of_my_model
+# ):  # FE : ColumnTransfromer, Pipeline
+#     "Cleaning, Processing and Feature Engineering of the input dataset."
+#     """:dataset pandas.DataFrame"""
+
+#     # if imputer is not None:
+#     #     output_dataset = imputer.transform(dataset)
+#     # else:
+#     #     output_dataset = dataset.copy()
+
+#     # output_dataset = scaler.transform(output_dataset)
+
+#     # if encoder is not None:
+#     #     output_dataset = encoder.transform(output_dataset)
+#     if FE is not None:
+#         output_dataset = FE.fit(output_dataset)
+
+#     return output_dataset
+
+
+
+def make_prediction(
+     Pclass, Sex, Age, SibSp,Parch, Fare, Embarked, PeopleInTicket, FarePerPerson,TicketNumber
+    
+):
+
+    df = pd.DataFrame(
+        [
+            [
+                PeopleInTicket,
+                Age,
+                FarePerPerson,
+                SibSp,
+                Pclass,
+                Fare,
+                Parch,
+                TicketNumber,
+                Embarked,
+                Sex,
+                
+            ]
+        ],
+        columns=num_cols + cat_cols,
+        
+    )
+    print(num_cols + cat_cols)
+    print( [
+                PeopleInTicket,
+                Age,
+                FarePerPerson,
+                SibSp,
+                Pclass,
+                Fare,
+                Parch,
+                TicketNumber,
+                Embarked,
+                Sex,
+                
+            ])
+        
+    X = df
+    #df[cat_cols] = df[cat_cols].astype("object")
+    output = pipeline_of_my_model.predict(X).tolist()
+    return output
+   
+   
+
+
+## Endpoints
+@app.post("/Titanic")
+async def predict(input: ModelInput):
+    """__descr__
+    --details---
+    """
+    output_pred = make_prediction(
+        PeopleInTicket =input.PeopleInTicket,
+        Age =input.Age,
+        FarePerPerson =input.FarePerPerson,
+        SibSp =input.SibSp,
+        Pclass =input.Pclass,
+        Fare =input.Fare,
+        Parch =input.Parch,
+        TicketNumber =input.TicketNumber,
+        Embarked =input.Embarked,
+        Sex=input.Sex,
+    )
+     # Labelling Model output
+    if output_pred == 0:
+        output_pred = "No,the person didn't survive"
+    else:
+        output_pred = "Yes,the person survived"
+    #return output_pred
+    return {
+        "prediction": output_pred,
+        "input": input
+    }
+
+
+# Execution
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "main:app",
+        reload=True,
+    )

src/asset/ml_component.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d101d8a21e78c89eb69eb4c51f54d0f5c45efda64d236046c72d0ee54483672
+size 154669

src/main.py

@@ -0,0 +1,158 @@
+from fastapi import FastAPI
+import pickle, uvicorn, os
+from pydantic import BaseModel
+import pandas as pd
+import numpy as np
+from sklearn import preprocessing
+from sklearn.impute import SimpleImputer
+from sklearn.compose import ColumnTransformer
+from sklearn.compose import make_column_selector as selector
+from sklearn.metrics import accuracy_score
+
+# Config & Setup
+## Variables of environment
+DIRPATH = os.path.dirname(__file__)
+ASSETSDIRPATH = os.path.join(DIRPATH, "asset")
+ml_component_pkl = os.path.join(ASSETSDIRPATH, "ml_component.pkl")
+
+print(
+    f" {'*'*10} Config {'*'*10}\n INFO: DIRPATH = {DIRPATH} \n INFO: ASSETSDIRPATH = {ASSETSDIRPATH} "
+)
+
+
+# API Basic config
+app = FastAPI(
+    title="Titanic Survivors API",
+    version="0.0.1",
+    description="Prediction of Titanic Survivors",
+)
+
+## Loading of assets
+with open(ml_component_pkl, "rb") as f:
+    loaded_items = pickle.load(f)
+#print("INFO:    Loaded assets:", loaded_items)
+
+pipeline_of_my_model = loaded_items["pipeline"]
+num_cols = loaded_items['numeric_columns']
+cat_cols = loaded_items['categorical_columns']
+
+## BaseModel
+class ModelInput(BaseModel):
+    PeopleInTicket: int
+    Age: float
+    FarePerPerson: float
+    SibSp: int
+    Pclass: int
+    Fare: float
+    Parch: int
+    TicketNumber: float
+    Embarked: str
+    Sex: str
+
+## Utils
+# def processing_FE(
+#     dataset, scaler, encoder,imputer, FE=pipeline_of_my_model
+# ):  # FE : ColumnTransfromer, Pipeline
+#     "Cleaning, Processing and Feature Engineering of the input dataset."
+#     """:dataset pandas.DataFrame"""
+
+#     # if imputer is not None:
+#     #     output_dataset = imputer.transform(dataset)
+#     # else:
+#     #     output_dataset = dataset.copy()
+
+#     # output_dataset = scaler.transform(output_dataset)
+
+#     # if encoder is not None:
+#     #     output_dataset = encoder.transform(output_dataset)
+#     if FE is not None:
+#         output_dataset = FE.fit(output_dataset)
+
+#     return output_dataset
+
+
+
+def make_prediction(
+     Pclass, Sex, Age, SibSp,Parch, Fare, Embarked, PeopleInTicket, FarePerPerson,TicketNumber
+    
+):
+
+    df = pd.DataFrame(
+        [
+            [
+                PeopleInTicket,
+                Age,
+                FarePerPerson,
+                SibSp,
+                Pclass,
+                Fare,
+                Parch,
+                TicketNumber,
+                Embarked,
+                Sex,
+                
+            ]
+        ],
+        columns=num_cols + cat_cols,
+        
+    )
+    print(num_cols + cat_cols)
+    print( [
+                PeopleInTicket,
+                Age,
+                FarePerPerson,
+                SibSp,
+                Pclass,
+                Fare,
+                Parch,
+                TicketNumber,
+                Embarked,
+                Sex,
+                
+            ])
+        
+    X = df
+    #df[cat_cols] = df[cat_cols].astype("object")
+    output = pipeline_of_my_model.predict(X).tolist()
+    return output
+   
+   
+
+
+## Endpoints
+@app.post("/Titanic")
+async def predict(input: ModelInput):
+    """__descr__
+    --details---
+    """
+    output_pred = make_prediction(
+        PeopleInTicket =input.PeopleInTicket,
+        Age =input.Age,
+        FarePerPerson =input.FarePerPerson,
+        SibSp =input.SibSp,
+        Pclass =input.Pclass,
+        Fare =input.Fare,
+        Parch =input.Parch,
+        TicketNumber =input.TicketNumber,
+        Embarked =input.Embarked,
+        Sex=input.Sex,
+    )
+     # Labelling Model output
+    if output_pred == 0:
+        output_pred = "No,the person didn't survive"
+    else:
+        output_pred = "Yes,the person survived"
+    #return output_pred
+    return {
+        "prediction": output_pred,
+        "input": input
+    }
+
+
+# Execution
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "main:app",
+        reload=True,
+    )