Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+
+# Load the symptom-disease dataset
+df = pd.read_csv("Symptom-severity.csv")
+
+# Create a bag-of-words representation of the symptoms
+vectorizer = CountVectorizer()
+X = vectorizer.fit_transform(df["Symptom"].values.astype("U"))
+y = df["Disease"]
+
+# Train a Naive Bayes classifier on the symptom-disease dataset
+clf = MultinomialNB()
+clf.fit(X, y)
+
+# Define the chatbot function
+def diagnose_disease(symptoms):
+    # Convert input symptoms to bag-of-words representation
+    X_new = vectorizer.transform([symptoms])
+
+    # Predict the disease using the trained classifier
+    disease = clf.predict(X_new)[0]
+
+    # Get the description of the predicted disease
+    description = df[df["Disease"] == disease]["Description"].values[0]
+
+    return f"The most likely disease based on the symptoms entered is {disease}. {description}"
+
+# Define the input and output interfaces
+input_text = gr.inputs.Textbox(label="Enter your symptoms separated by commas")
+output_text = gr.outputs.Textbox()
+
+# Create the Gradio interface
+gr.Interface(fn=diagnose_disease, inputs=input_text, outputs=output_text, 
+             title="Symptom-based Disease Diagnosis Chatbot", 
+             description="Enter your symptoms separated by commas, and the chatbot will predict the most likely disease.").launch()