completed API

IT20151010-API/ClassificationModel.yaml

+name: ClassificationModels
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - asttokens=2.0.5=pyhd3eb1b0_0
+  - backcall=0.2.0=pyhd3eb1b0_0
+  - blas=1.0=mkl
+  - bottleneck=1.3.5=py39h080aedc_0
+  - brotli=1.0.9=h2bbff1b_7
+  - brotli-bin=1.0.9=h2bbff1b_7
+  - ca-certificates=2023.05.30=haa95532_0
+  - certifi=2023.7.22=py39haa95532_0
+  - cmdstan=2.31.0=h58f7c43_0
+  - cmdstanpy=1.1.0=py39haa95532_0
+  - colorama=0.4.6=py39haa95532_0
+  - comm=0.1.2=py39haa95532_0
+  - convertdate=2.3.2=pyhd3eb1b0_0
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - debugpy=1.6.7=py39hd77b12b_0
+  - decorator=5.1.1=pyhd3eb1b0_0
+  - ephem=4.1.2=py39h2bbff1b_0
+  - executing=0.8.3=pyhd3eb1b0_0
+  - freetype=2.12.1=ha860e81_0
+  - giflib=5.2.1=h8cc25b3_3
+  - glib=2.69.1=h5dc1a3c_2
+  - gst-plugins-base=1.18.5=h9e645db_0
+  - gstreamer=1.18.5=hd78058f_0
+  - holidays=0.29=py39haa95532_0
+  - icu=58.2=ha925a31_3
+  - importlib-metadata=6.0.0=py39haa95532_0
+  - importlib_metadata=6.0.0=hd3eb1b0_0
+  - importlib_resources=5.2.0=pyhd3eb1b0_1
+  - intel-openmp=2023.1.0=h59b6b97_46319
+  - ipykernel=6.25.0=pyh6817e22_0
+  - ipython=8.12.2=py39haa95532_0
+  - jedi=0.18.1=py39haa95532_1
+  - jpeg=9e=h2bbff1b_1
+  - jupyter_client=8.1.0=py39haa95532_0
+  - jupyter_core=5.3.0=py39haa95532_0
+  - kiwisolver=1.4.4=py39hd77b12b_0
+  - krb5=1.19.4=h5b6d351_0
+  - lerc=3.0=hd77b12b_0
+  - libbrotlicommon=1.0.9=h2bbff1b_7
+  - libbrotlidec=1.0.9=h2bbff1b_7
+  - libbrotlienc=1.0.9=h2bbff1b_7
+  - libclang=14.0.6=default_hb5a9fac_1
+  - libclang13=14.0.6=default_h8e68704_1
+  - libdeflate=1.17=h2bbff1b_0
+  - libffi=3.4.4=hd77b12b_0
+  - libiconv=1.16=h2bbff1b_2
+  - libogg=1.3.5=h2bbff1b_1
+  - libpng=1.6.39=h8cc25b3_0
+  - libsodium=1.0.18=h62dcd97_0
+  - libtiff=4.5.0=h6c2663c_2
+  - libvorbis=1.3.7=he774522_0
+  - libwebp=1.2.4=hbc33d0d_1
+  - libwebp-base=1.2.4=h2bbff1b_1
+  - libxml2=2.10.3=h0ad7f3c_0
+  - libxslt=1.1.37=h2bbff1b_0
+  - lunarcalendar=0.0.9=pyhd3eb1b0_0
+  - lz4-c=1.9.4=h2bbff1b_0
+  - m2-bash=4.3.042=5
+  - m2-coreutils=8.25=102
+  - m2-filesystem=2016.04=4
+  - m2-gcc-libs=5.3.0=4
+  - m2-gmp=6.1.0=3
+  - m2-libiconv=1.14=3
+  - m2-libintl=0.19.7=4
+  - m2-msys2-runtime=2.5.0.17080.65c939c=3
+  - m2-sed=4.2.2=3
+  - m2w64-binutils=2.25.1=5
+  - m2w64-bzip2=1.0.6=6
+  - m2w64-crt-git=5.0.0.4636.2595836=2
+  - m2w64-gcc=5.3.0=6
+  - m2w64-gcc-ada=5.3.0=6
+  - m2w64-gcc-fortran=5.3.0=6
+  - m2w64-gcc-libgfortran=5.3.0=6
+  - m2w64-gcc-libs=5.3.0=7
+  - m2w64-gcc-libs-core=5.3.0=7
+  - m2w64-gcc-objc=5.3.0=6
+  - m2w64-gmp=6.1.0=2
+  - m2w64-headers-git=5.0.0.4636.c0ad18a=2
+  - m2w64-isl=0.16.1=2
+  - m2w64-libiconv=1.14=6
+  - m2w64-libmangle-git=5.0.0.4509.2e5a9a2=2
+  - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+  - m2w64-make=4.1.2351.a80a8b8=2
+  - m2w64-mpc=1.0.3=3
+  - m2w64-mpfr=3.1.4=4
+  - m2w64-pkg-config=0.29.1=2
+  - m2w64-toolchain=5.3.0=7
+  - m2w64-toolchain_win-64=2.4.0=0
+  - m2w64-tools-git=5.0.0.4592.90b8472=2
+  - m2w64-windows-default-manifest=6.4=3
+  - m2w64-winpthreads-git=5.0.0.4634.697f757=2
+  - m2w64-zlib=1.2.8=10
+  - matplotlib-base=3.7.1=py39hf11a4ad_1
+  - matplotlib-inline=0.1.6=py39haa95532_0
+  - mkl=2023.1.0=h6b88ed4_46357
+  - mkl-service=2.4.0=py39h2bbff1b_1
+  - mkl_fft=1.3.6=py39hf11a4ad_1
+  - mkl_random=1.2.2=py39hf11a4ad_1
+  - msys2-conda-epoch=20160418=1
+  - munkres=1.1.4=py_0
+  - nest-asyncio=1.5.6=py39haa95532_0
+  - numexpr=2.8.4=py39h7b80656_1
+  - numpy=1.25.2=py39h055cbcc_0
+  - numpy-base=1.25.2=py39h65a83cf_0
+  - openssl=1.1.1v=h2bbff1b_0
+  - packaging=23.0=py39haa95532_0
+  - parso=0.8.3=pyhd3eb1b0_0
+  - pcre=8.45=hd77b12b_0
+  - pickleshare=0.7.5=pyhd3eb1b0_1003
+  - pip=23.2.1=py39haa95532_0
+  - platformdirs=2.5.2=py39haa95532_0
+  - ply=3.11=py39haa95532_0
+  - prompt-toolkit=3.0.36=py39haa95532_0
+  - prophet=1.1.4=py39h28ab207_0
+  - psutil=5.9.0=py39h2bbff1b_0
+  - pure_eval=0.2.2=pyhd3eb1b0_0
+  - pygments=2.15.1=py39haa95532_1
+  - pymeeus=0.5.11=pyhd3eb1b0_1
+  - pyparsing=3.0.9=py39haa95532_0
+  - pyqt=5.15.7=py39hd77b12b_0
+  - pyqt5-sip=12.11.0=py39hd77b12b_0
+  - python=3.9.17=h6244533_0
+  - python-dateutil=2.8.2=pyhd3eb1b0_0
+  - pytz=2022.7=py39haa95532_0
+  - pywin32=305=py39h2bbff1b_0
+  - pyzmq=25.1.0=py39hd77b12b_0
+  - qt-main=5.15.2=he8e5bd7_8
+  - qt-webengine=5.15.9=hb9a9bb5_5
+  - qtwebkit=5.212=h2bbfb41_5
+  - setuptools=68.0.0=py39haa95532_0
+  - sip=6.6.2=py39hd77b12b_0
+  - six=1.16.0=pyhd3eb1b0_1
+  - sqlite=3.41.2=h2bbff1b_0
+  - stack_data=0.2.0=pyhd3eb1b0_0
+  - tbb=2021.8.0=h59b6b97_0
+  - tk=8.6.12=h2bbff1b_0
+  - toml=0.10.2=pyhd3eb1b0_0
+  - tornado=6.3.2=py39h2bbff1b_0
+  - tqdm=4.65.0=py39hd4e2768_0
+  - traitlets=5.7.1=py39haa95532_0
+  - typing_extensions=4.7.1=py39haa95532_0
+  - vc=14.2=h21ff451_1
+  - vs2015_runtime=14.27.29016=h5e58377_2
+  - wcwidth=0.2.5=pyhd3eb1b0_0
+  - wheel=0.38.4=py39haa95532_0
+  - xz=5.4.2=h8cc25b3_0
+  - zeromq=4.3.4=hd77b12b_0
+  - zipp=3.11.0=py39haa95532_0
+  - zlib=1.2.13=h8cc25b3_0
+  - zstd=1.5.5=hd43e919_0
+  - pip:
+      - annotated-types==0.5.0
+      - anyio==3.7.1
+      - catboost==1.2
+      - charset-normalizer==3.2.0
+      - click==8.1.6
+      - contourpy==1.1.0
+      - exceptiongroup==1.1.2
+      - faker==19.3.0
+      - fastapi==0.101.0
+      - fonttools==4.42.0
+      - h11==0.14.0
+      - idna==3.4
+      - imbalanced-learn==0.11.0
+      - imblearn==0.0
+      - importlib-resources==6.0.0
+      - ipywidgets==8.1.0
+      - joblib==1.3.1
+      - jupyterlab-widgets==3.0.8
+      - matplotlib==3.7.2
+      - pandas==2.0.3
+      - pillow==10.0.0
+      - plotly==5.15.0
+      - pydantic==2.1.1
+      - pydantic-core==2.4.0
+      - python-graphviz==0.20.1
+      - python-multipart==0.0.6
+      - requests==2.31.0
+      - scikit-learn==1.3.0
+      - scipy==1.11.1
+      - seaborn==0.12.2
+      - sniffio==1.3.0
+      - starlette==0.27.0
+      - svm==0.1.0
+      - tenacity==8.2.2
+      - threadpoolctl==3.2.0
+      - tzdata==2023.3
+      - urllib3==2.0.4
+      - uvicorn==0.23.2
+      - widgetsnbextension==4.0.8
+      - xgboost==1.7.6
+      - xmltodict==0.13.0

IT20151010-API/main.py

+import uvicorn
+from fastapi import FastAPI,Depends,File, UploadFile,HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import pandas as pd
+import pickle
+from model_loader import load_model,DecisionTree_model,Nephrotic_model,CKD_model,AKI_model
+from prophet import Prophet
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from typing import Optional
+from pydantic import BaseModel,ValidationError
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+    allow_credentials=True,
+)
+
+
+def get_loaded_model():
+    model = load_model()
+    return model
+
+def get_DecisionTree_model():
+    model = DecisionTree_model()
+    return model
+
+@app.on_event("startup")
+async def startup_event():
+    app.loaded_model = get_loaded_model()
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+
+
+class InputData(BaseModel):
+    age: int
+    blood_pressure: float
+    specific_gravity: float
+    albumin: float
+    sugar: float
+    red_blood_cells: int
+    pus_cell: int
+    pus_cell_clumps: int
+    bacteria: int
+    blood_glucose_random: float
+    blood_urea: float
+    serum_creatinine: float
+    sodium: float
+    potassium: float
+    haemoglobin: float
+    packed_cell_volume: float
+    white_blood_cell_count: float
+    red_blood_cell_count: float
+    hypertension: int
+    diabetes_mellitus: int
+    coronary_artery_disease: int
+    appetite: int
+    peda_edema: int
+    aanemia: int
+
+@app.post("/predict_ckd/")
+async def predict_ckd(input_data: InputData,loaded_model=Depends(get_loaded_model)):
+    # Validate input_data using Pydantic models.
+    print(input_data.age)
+    print(input_data.blood_pressure)
+    print(input_data.peda_edema)
+    data = pd.DataFrame({
+        'age': [input_data.age],
+        'blood_pressure': [input_data.blood_pressure],
+        'specific_gravity': [input_data.specific_gravity],
+        'albumin': [input_data.albumin],
+        'sugar': [input_data.sugar],
+        'red_blood_cells': [input_data.red_blood_cells],
+        'pus_cell': [input_data.pus_cell],
+        'pus_cell_clumps': [input_data.pus_cell_clumps],
+        'bacteria': [input_data.bacteria],
+        'blood_glucose_random': [input_data.blood_glucose_random],
+        'blood_urea': [input_data.blood_urea],
+        'serum_creatinine': [input_data.serum_creatinine],
+        'sodium': [input_data.sodium],
+        'potassium': [input_data.potassium],
+        'haemoglobin': [input_data.haemoglobin],
+        'packed_cell_volume': [input_data.packed_cell_volume],
+        'white_blood_cell_count': [input_data.white_blood_cell_count],
+        'red_blood_cell_count': [input_data.red_blood_cell_count],
+        'hypertension': [input_data.hypertension],
+        'diabetes_mellitus': [input_data.diabetes_mellitus],
+        'coronary_artery_disease': [input_data.coronary_artery_disease],
+        'appetite': [input_data.appetite],
+        'peda_edema': [input_data.peda_edema],
+        'aanemia': [input_data.aanemia]
+    })
+
+    new_pred_rf_clf = loaded_model.predict(data)
+    print(new_pred_rf_clf)
+    return int(new_pred_rf_clf)
+
+
+def Drg_forecast(df):
+    m = Prophet()
+    m.fit(df)
+    future = m.make_future_dataframe(3, freq='MS')
+    forecast = m.predict(future)
+    forecast = forecast[['ds','yhat']].tail(3)
+    # forecast.to_csv("last3.csv")
+    result = []
+    for index, row in forecast.iterrows():
+        timestamp = pd.Timestamp(row['ds'])
+        date_only = timestamp.date()
+        rounded_yhat = round(row['yhat'])
+        result.append({'date': str(date_only), 'prediction': rounded_yhat})
+    return result
+
+@app.post("/uploadcsv/")
+def upload_csv(csv_file: UploadFile = File(...)):
+    dataframe = pd.read_csv(csv_file.file)
+    dataframe.columns = ['ds', 'y']
+    predictions = Drg_forecast(dataframe)
+    print(predictions)
+    return predictions
+
+class InputDataForecast(BaseModel):
+    date:str
+    sales:int
+
+@app.post("/forecast/")
+def forecast_via_values(inputdataF :InputDataForecast):
+    data = pd.DataFrame({'ds':[inputdataF.date],'y':[inputdataF.sales]})
+    df = pd.read_csv('drg1.csv')
+    df.columns = ['ds', 'y']
+    frames = [df, data]
+    dataframe = pd.concat(frames)
+    predictions = Drg_forecast(dataframe)
+    print(predictions)
+    return predictions
+
+def recommend_diet(patient_data,loaded_model):  
+    patient_df = pd.DataFrame([patient_data])
+    
+    gender_mapping = {'M': 0, 'F': 1}
+    conditions_mapping = {'Hypertension': 0, 'Diabetes': 1, 'nan': 2}
+    food_mapping = {'Low Sodium': 0, 'Low Potassium': 1, 'Balanced': 2}
+
+    patient_df['Gender'] = patient_df['Gender'].map(gender_mapping)
+    patient_df['Other Conditions'] = patient_df['Other Conditions'].map(conditions_mapping)
+    patient_df['Preferred Food'] = patient_df['Preferred Food'].map(food_mapping)
+
+    # Predict the kidney disease stage
+    level = loaded_model.predict(patient_df)[0]
+    print(level)
+
+    if level == 1:
+        return "Maintain a balanced diet. No specific restrictions but ensure a healthy intake of protein, sodium, potassium and phosphorus."
+    elif level == 2:
+        return "Start to monitor and limit the intake of protein, sodium, potassium and phosphorus to prevent further kidney damage."
+    elif level == 3:
+        return "Follow a diet low in protein, sodium, potassium and phosphorus. Consult a dietitian for a personalized diet plan."
+
+
+class InputDataDiet_Plan(BaseModel):
+    Age: int
+    Gender: str
+    BMI: int
+    Current_Protein_Intake: int
+    Current_Sodium_Intake: int
+    Current_Potassium_Intake: int
+    Current_Phosphorus_Intake: int
+    Other_Conditions: str
+    GFR: int
+    Proteinuria: int
+    Preferred_Food: str
+
+@app.post("/Suggest_Diet_Plan/")
+def Suggest_Diet_Plan(
+    input_Data_Diet_Plan:InputDataDiet_Plan,
+    loaded_model_1=Depends(get_DecisionTree_model)
+):
+    patient_data = {
+    'Age': input_Data_Diet_Plan.Age,
+    'Gender': input_Data_Diet_Plan.Gender,
+    'BMI': input_Data_Diet_Plan.BMI,
+    'Current Protein Intake (g)': input_Data_Diet_Plan.Current_Protein_Intake,
+    'Current Sodium Intake (mg)': input_Data_Diet_Plan.Current_Sodium_Intake,
+    'Current Potassium Intake (mg)': input_Data_Diet_Plan.Current_Potassium_Intake,
+    'Current Phosphorus Intake (mg)': input_Data_Diet_Plan.Current_Phosphorus_Intake,
+    'Other Conditions': input_Data_Diet_Plan.Other_Conditions,
+    'GFR': input_Data_Diet_Plan.GFR,
+    'Proteinuria':input_Data_Diet_Plan.Proteinuria,
+    'Preferred Food': input_Data_Diet_Plan.Preferred_Food,
+    }
+    suggesting = recommend_diet(patient_data,loaded_model_1)
+    print(suggesting)
+    return suggesting
+
+class Symptoms(BaseModel):
+    Age: int
+    Gender: str
+    AKIDiagnosis: str
+    InitialCreatinine: float
+    PeakCreatinine: float
+    UrineOutput: int
+
+    Proteinuria: str
+    Edema: str
+    Albumin_Level: str
+
+    Change_in_Urination: int
+    Swelling: int
+
+    Metallic_Taste_in_Mouth: int
+
+    Dizziness_Trouble_Concentrating: int
+    Pain_in_Back_or_Sides: int
+    Nausea_Vomiting: int
+
+@app.post("/predict_diseas/")
+def predict_disease(
+    inputDData:Symptoms,
+
+    Model1=Depends(AKI_model),
+    Model2=Depends(Nephrotic_model),
+    Model3=Depends(CKD_model),
+):
+    if (
+        inputDData.AKIDiagnosis != "" and
+        inputDData.InitialCreatinine is not None and
+        inputDData.PeakCreatinine is not None and
+        inputDData.UrineOutput is not None and
+        inputDData.Swelling is not None
+    ):
+        patient_data = {
+            "AKIDiagnosis" :inputDData.AKIDiagnosis,
+            "InitialCreatinine" :inputDData.InitialCreatinine,
+            "PeakCreatinine" :inputDData.PeakCreatinine,
+            "UrineOutput" :inputDData.UrineOutput,
+            "Swelling" :inputDData.Swelling,
+        }
+        print("11")
+        results = Model_1(patient_data,Model1)
+        return results
+    elif (
+        inputDData.Change_in_Urination is not None or
+        inputDData.Swelling is not None or
+        inputDData.Metallic_Taste_in_Mouth is not None or
+        inputDData.Dizziness_Trouble_Concentrating is not None or
+        inputDData.Pain_in_Back_or_Sides is not None or
+        inputDData.Nausea_Vomiting is not None
+    ):    
+        patient_data = {
+            'Change_in_Urination': inputDData.Change_in_Urination,
+            'Metallic_Taste_in_Mouth': inputDData.Metallic_Taste_in_Mouth,
+            'Dizziness_Trouble_Concentrating': inputDData.Dizziness_Trouble_Concentrating,
+            'Pain_in_Back_or_Sides': inputDData.Pain_in_Back_or_Sides,
+            'Nausea_Vomiting': inputDData.Nausea_Vomiting,
+        }
+        print("11")
+        results = Model_3(patient_data,Model3)
+        return results
+    else:
+        return "Invalid input parameters"
+
+def Model_1(patient_data,model):
+    patient_data = pd.DataFrame([patient_data])
+    AKIDiagnosis = {'Yes': 0, 'No': 1}
+    # Swelling = {'Yes': 0, 'No': 1}
+    patient_data['AKIDiagnosis'] = patient_data['AKIDiagnosis'].map(AKIDiagnosis)
+    # patient_data['Swelling'] = patient_data['Swelling'].map(Swelling)
+    results = model.predict(patient_data)
+    if results==0:
+        results = "Has AKI"
+    else:
+        results = "no AKI"
+    return results
+
+def Model_3(patient_data,model):
+    patient_data = pd.DataFrame([patient_data])
+    results = model.predict(patient_data)[0]
+    if results==0:
+        results = "Has CKD"
+    else:
+        results = "no CKD"
+    return results
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="127.0.0.1",port=8000, log_level="info")
+

IT20151010-API/model_loader.py

+import pickle
+
+def load_model():
+    with open('rf_clf_model.pkl', 'rb') as model_file:
+        model = pickle.load(model_file)
+    return model
+
+def DecisionTree_model():
+    with open('DTC_model.pkl', 'rb') as model_file:
+        model = pickle.load(model_file)
+    return model    
+
+def Nephrotic_model():
+    with open('Random Forest_Nephrotic_model.pkl', 'rb') as model_file:
+        model = pickle.load(model_file)
+    return model   
+
+def CKD_model():
+    with open('SVM_model.pkl', 'rb') as model_file:
+        model = pickle.load(model_file)
+    return model
+
+def AKI_model():
+    with open('Random Forest_Aki_model.pkl', 'rb') as model_file:
+        model = pickle.load(model_file)
+    return model