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Browse files- app.py +48 -0
- model.pkl +3 -0
- requierments.txt +3 -0
app.py
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# import the necessary packages
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import gradio as gr
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import pickle
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import pandas as pd
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# Load the saved model using pickle
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with open(r'Stroke_Prediction/model.pkl', 'rb') as file:
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model = pickle.load(file)
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# Define a function to make predictions
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def predict(gender, age, hypertension, heart_disease, ever_married, work_type, residence_type, avg_glucose_level, bmi, smoking_status):
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# Convert the input into a pandas DataFrame
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input_df = pd.DataFrame([[gender, age, hypertension, heart_disease, ever_married, work_type, residence_type, avg_glucose_level, bmi, smoking_status]],
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columns=['gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type',
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'Residence_type', 'avg_glucose_level', 'bmi', 'smoking_status'])
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# Predict the stroke probability
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prediction = model.predict_proba(input_df)[0][1]
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# Return the prediction
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result = "The probability of stroke is {:.2f}%".format(prediction * 100) #to give a percentage
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return result
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# Create the input and output interfaces
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gender = gr.inputs.Radio(choices=["Male", "Female"], label="Gender")
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age = gr.inputs.Slider(minimum=0, maximum=100)
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hypertension = gr.inputs.Radio(choices=["Yes", "No"], label="Hypertension")
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heart_disease = gr.inputs.Radio(choices=["Yes", "No"], label="Heart Disease")
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ever_married = gr.inputs.Radio(choices=["Yes", "No"], label="Ever Married")
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work_type = gr.inputs.Dropdown(
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["Private", "Self-employed", "Govt_job", "Children", "Never_worked"], label="Work Type")
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residence_type = gr.inputs.Radio(
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choices=["Urban", "Rural"], label="Residence Type")
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avg_glucose_level = gr.inputs.Number(label="Average Glucose Level")
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bmi = gr.inputs.Slider(minimum=0, maximum=60, label="BMI")
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smoking_status = gr.inputs.Dropdown(
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["formerly smoked", "never smoked", "smokes"], label="Smoking Status")
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# Create the interface
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inputs = [gender, age, hypertension, heart_disease, ever_married,
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work_type, residence_type, avg_glucose_level, bmi, smoking_status]
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outputs = gr.outputs.Textbox(label="Stroke Probability")
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# Launch the interface
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demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title="Stroke Prediction",
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description="Fill in the details and click submit to check the probability of stroke")
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demo.launch(share=True)
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model.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:688d2f5b4ab52ec8fd1b4634c51428e71a1c82eddd943e6a849288673bebd0b6
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size 257370
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requierments.txt
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gradio
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pickle
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pandas
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