Create app.py

app.py

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+import streamlit as st
+import joblib
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+
+# Load the trained KNN model
+knn_classifier = joblib.load('knn_model.pkl')
+
+# Load the TF-IDF vectorizer
+tfidf_vectorizer = joblib.load('tfidf_vectorizer.pkl')
+
+# Download nltk resources
+nltk.download('punkt')
+nltk.download('stopwords')
+
+# Text Preprocessing Function
+stop_words = set(stopwords.words('english'))
+
+def preprocess_text(text):
+    words = word_tokenize(text.lower())
+    words = [word for word in words if word.isalpha() and word not in stop_words]
+    return ' '.join(words)
+
+# Inference function
+def predict_disease(symptom):
+    preprocessed_symptom = preprocess_text(symptom)
+    symptom_tfidf = tfidf_vectorizer.transform([preprocessed_symptom])
+    predicted_disease = knn_classifier.predict(symptom_tfidf)
+    return predicted_disease[0]
+
+# Streamlit UI
+st.title("Disease Classification using Symptoms")
+st.markdown("Enter your symptoms to predict the disease.")
+
+# Input box for symptoms
+symptom = st.text_input("Enter symptoms:", "")
+
+# Predict button
+if st.button("Predict Disease"):
+    if symptom:
+        predicted_disease = predict_disease(symptom)
+        st.success(f"Predicted Disease: {predicted_disease}")
+    else:
+        st.warning("Please enter symptoms.")
+
+# Add some styling
+st.markdown(
+    """
+    <style>
+    .main .block-container {
+        max-width: 600px;
+        padding-top: 2rem;
+        padding-right: 2rem;
+        padding-left: 2rem;
+        padding-bottom: 2rem;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)