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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+doctor.png filter=lfs diff=lfs merge=lfs -text

caner_prediction.py

@@ -0,0 +1,82 @@
+import gradio as gr
+import numpy as np
+import urllib.request
+
+# Downloading the image and saving it in the local folder
+# urllib.request.urlretrieve('https://cdn.discordapp.com/attachments/682736875483430985/1089711182094553108/Dopeshott_young_black_femal_doctor_19e26be2-6801-4aed-b481-793b1e77a984.png', 'doctor.png')
+
+# Dummy data
+data = np.array([
+    [65, 1, 1, 1, 0.5],
+    [45, 0, 0, 0, 0.2],
+    [70, 1, 0, 1, 0.8],
+    [55, 0, 1, 0, 0.6],
+    [50, 1, 1, 1, 0.7]
+])
+
+features = ['Age', 'Sex', 'Smoking', 'Treatment', 'Chemotherapy']
+sex_options = ['Male', 'Female']
+smoking_options = ['No', 'Yes']
+treatment_options = ['No', 'Yes']
+chemo_options = ['No', 'Yes']
+
+# Function to predict cancer
+def predict_cancer(age, sex, smoking, treatment, chemotherapy):
+    # Format input
+    sex = 0 if sex == 'Male' else 1
+    smoking = 0 if smoking == 'No' else 1
+    treatment = 0 if treatment == 'No' else 1
+    chemotherapy = 0 if chemotherapy == 'No' else 1
+    input_data = np.array([[age, sex, smoking, treatment, chemotherapy]])
+    # Dummy prediction
+    prob_alive = np.random.choice([0.8, 0.7, 0.65, 0.548, 0.78])
+    prediction = 'Alive' if prob_alive >= 0.5 else 'Dead'
+    return f'{prediction} with a survival probability of {prob_alive:.3f}'
+
+# Interface
+age_slider = gr.inputs.Slider(minimum=20, maximum=90, step=1, label='Age')
+sex_dropdown = gr.inputs.Dropdown(choices=sex_options, label='Sex')
+smoking_dropdown = gr.inputs.Dropdown(choices=smoking_options, label='Smoking')
+treatment_dropdown = gr.inputs.Dropdown(choices=treatment_options, label='Treatment')
+chemo_dropdown = gr.inputs.Dropdown(choices=chemo_options, label='Chemotherapy')
+inputs = [age_slider, sex_dropdown, smoking_dropdown, treatment_dropdown, chemo_dropdown]
+outputs = gr.outputs.Textbox(label='Prediction')
+
+css = """
+.sidebar-content {
+    padding: 20px;
+    background-color: #f5f5f5;
+    box-shadow: 1px 1px 10px #ccc;
+    margin-bottom: 20px;
+    border-radius: 10px;
+}
+.doctor-img-container {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+.doctor-img {
+    height: auto;
+    width: auto;
+    max-width: 100%;
+    max-height: 100%;
+}
+"""
+
+sidebar = [
+    # Adding image of a doctor to the sidebar
+    '<div class="doctor-img-container"><img class="doctor-img" src="doctor.png" alt="A medical doctor"></div>',
+    'This is a cancer prediction app',
+]
+
+iface = gr.Interface(
+    fn=predict_cancer,
+    inputs=inputs,
+    outputs=outputs,
+    title='Cancer Predictor',
+    description='Predicts the risk of survival in cancer patients.\nChoose the input values and the prediction will be displayed automatically.',
+    sidebar=sidebar,
+    css=css
+)
+
+iface.launch()