Create app.py

app.py

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+import pickle
+import gradio as gr
+
+# Load the trained model
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Mapping
+diagnoses = {
+    0: "Negative",
+    1: "Hypothyroid",
+    2: "Hyperthyroid"
+}
+
+# Preprocess inputs
+def preprocess_inputs(age, sex, on_thyroxine, query_on_thyroxine, on_antithyroid_meds, sick, pregnant,
+                      thyroid_surgery, I131_treatment, query_hypothyroid, query_hyperthyroid, lithium,
+                      goitre, tumor, hypopituitary, psych, TSH, T3, TT4, T4U, FTI):
+    
+    binary_map = {'Yes': 1, 'No': 0, '': None}
+    
+    on_thyroxine = binary_map[on_thyroxine]
+    query_on_thyroxine = binary_map[query_on_thyroxine]
+    on_antithyroid_meds = binary_map[on_antithyroid_meds]
+    sick = binary_map[sick]
+    pregnant = binary_map[pregnant]
+    thyroid_surgery = binary_map[thyroid_surgery]
+    I131_treatment = binary_map[I131_treatment]
+    query_hypothyroid = binary_map[query_hypothyroid]
+    query_hyperthyroid = binary_map[query_hyperthyroid]
+    lithium = binary_map[lithium]
+    goitre = binary_map[goitre]
+    tumor = binary_map[tumor]
+    hypopituitary = binary_map[hypopituitary]
+    psych = binary_map[psych]
+    
+    sex = 1 if sex == 'F' else 0 if sex == 'M' else None
+
+    return [age, sex, on_thyroxine, query_on_thyroxine, on_antithyroid_meds, sick, pregnant,
+            thyroid_surgery, I131_treatment, query_hypothyroid, query_hyperthyroid, lithium,
+            goitre, tumor, hypopituitary, psych, TSH, T3, TT4, T4U, FTI]
+
+# Predict function
+def predict_diagnosis(age, sex, on_thyroxine, query_on_thyroxine, on_antithyroid_meds, sick, pregnant,
+                      thyroid_surgery, I131_treatment, query_hypothyroid, query_hyperthyroid, lithium,
+                      goitre, tumor, hypopituitary, psych, TSH, T3, TT4, T4U, FTI):
+    
+    inputs = preprocess_inputs(age, sex, on_thyroxine, query_on_thyroxine, on_antithyroid_meds, sick,
+                               pregnant, thyroid_surgery, I131_treatment, query_hypothyroid,
+                               query_hyperthyroid, lithium, goitre, tumor, hypopituitary, psych,
+                               TSH, T3, TT4, T4U, FTI)
+    
+    output = model.predict([inputs])[0]
+    return diagnoses.get(output, "Unknown")
+
+# Define Gradio interface
+demo = gr.Interface(
+    fn=predict_diagnosis,
+    inputs=[
+        gr.Number(label="Age"),
+        gr.Radio(["M", "F"], label="Sex"),
+        gr.Radio(["Yes", "No"], label="On Thyroxine"),
+        gr.Radio(["Yes", "No"], label="Query on Thyroxine"),
+        gr.Radio(["Yes", "No"], label="On Antithyroid Meds"),
+        gr.Radio(["Yes", "No"], label="Sick"),
+        gr.Radio(["Yes", "No"], label="Pregnant"),
+        gr.Radio(["Yes", "No"], label="Thyroid Surgery"),
+        gr.Radio(["Yes", "No"], label="I131 Treatment"),
+        gr.Radio(["Yes", "No"], label="Query Hypothyroid"),
+        gr.Radio(["Yes", "No"], label="Query Hyperthyroid"),
+        gr.Radio(["Yes", "No"], label="Lithium"),
+        gr.Radio(["Yes", "No"], label="Goitre"),
+        gr.Radio(["Yes", "No"], label="Tumor"),
+        gr.Radio(["Yes", "No"], label="Hypopituitary"),
+        gr.Radio(["Yes", "No"], label="Psych"),
+        gr.Number(label="TSH"),
+        gr.Number(label="T3"),
+        gr.Number(label="TT4"),
+        gr.Number(label="T4U"),
+        gr.Number(label="FTI"),
+    ],
+    outputs="text",
+    title="Thyroid Diagnosis Predictor",
+    description="Predict whether a patient has Hypothyroid, Hyperthyroid or is Negative using medical features."
+)
+
+
+# Launch app
+if __name__ == "__main__":
+    demo.launch()