Duplicate from Jishnnu/Detecting_COVID-19

Co-authored-by: Jishnu Pillai Anilkumar <[email protected]>

.gitattributes

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README.md

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+---
+title: Detecting COVID-19
+emoji: 🚀
+colorFrom: gray
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.29.0
+app_file: app.py
+pinned: false
+duplicated_from: Jishnnu/Detecting_COVID-19
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ann_model.h5

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app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+from sklearn.preprocessing import LabelEncoder
+
+# Load the dataset and get the column names
+dataset = pd.read_csv('Covid Dataset.csv')
+columns_to_drop = ['Wearing Masks', 'Sanitization from Market']
+dataset = dataset.drop(columns_to_drop, axis=1)
+column_names = dataset.columns.tolist()
+
+# Define a function to make predictions using the selected models
+def predict_covid(*symptoms):
+    # Convert None values to Falses
+    symptoms = [False if symptom is None else symptom for symptom in symptoms]
+    
+    if sum(symptoms) == 0:
+        return "COVID-19 Negative"
+    
+    # Load the saved models
+    model_logreg = joblib.load('logreg_model.h5')
+    model_rf_classifier = joblib.load('rf_classifier_model.h5')
+    model_dt_classifier = joblib.load('dt_classifier_model.h5')
+    model_knn_classifier = joblib.load('knn_classifier_model.h5')
+    model_svm_classifier = joblib.load('svm_classifier_model.h5')
+    model_ann_classifier = joblib.load('ann_model.h5')
+    voting_classifier = joblib.load('voting_classifier_model.h5')
+    stacking_classifier = joblib.load('stacking_classifier_model.h5')
+
+    # Prepare the input data
+    label_encoder = LabelEncoder()
+    input_data = pd.DataFrame([list(symptoms)], columns=column_names[:-1])
+    encoded_input_data = input_data.copy()
+    for column in encoded_input_data.columns:
+        if encoded_input_data[column].dtype == object:
+            encoded_input_data[column] = label_encoder.transform(encoded_input_data[column])
+
+    # Make predictions using the selected models
+    logreg_prediction = int(model_logreg.predict(encoded_input_data)[0])
+    rf_prediction = int(model_rf_classifier.predict(encoded_input_data)[0])
+    dt_prediction = int(model_dt_classifier.predict(encoded_input_data)[0])
+    knn_prediction = int(model_knn_classifier.predict(encoded_input_data)[0])
+    svm_prediction = int(model_svm_classifier.predict(encoded_input_data)[0])
+    ann_prediction = int(model_ann_classifier.predict(encoded_input_data)[0])
+    voting_prediction = int(voting_classifier.predict(encoded_input_data)[0])
+    stacking_prediction = int(stacking_classifier.predict(encoded_input_data)[0])
+
+    # Determine the overall prediction
+    prediction = 1 if sum([logreg_prediction, rf_prediction, dt_prediction, knn_prediction,
+                          svm_prediction, ann_prediction, voting_prediction, stacking_prediction]) >= 4 else 0
+
+    # Return the prediction
+    return "COVID-19 Positive" if prediction == 1 else "COVID-19 Negative"
+
+# Create a list of checkboxes for the dataset columns
+checkboxes = [gr.inputs.Checkbox(label=column_name) for column_name in column_names[:-1]]
+
+# Create the input interface with the checkboxes
+inputs = checkboxes
+
+# Create the output interface with the predicted labels
+outputs = gr.outputs.Label(num_top_classes=1, label="COVID-19 Status")
+title = "COVID-19 Detection"
+description = "Select your symptoms and contact history to check if you have COVID-19"
+final_model = gr.Interface(fn=predict_covid, inputs=inputs, outputs=outputs, title=title, description=description)
+
+# Create the Gradio app
+if __name__ == '__main__':
+    final_model.launch(inline=False)

dt_classifier_model.h5

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requirements.txt

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+gradio
+pandas
+joblib
+scikit-learn

rf_classifier_model.h5

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voting_classifier_model.h5

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