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| import streamlit as st | |
| import pickle | |
| import nltk | |
| from nltk.corpus import stopwords | |
| from nltk.tokenize import word_tokenize | |
| # Download NLTK resources | |
| nltk.download('punkt', quiet=True) | |
| nltk.download('stopwords', quiet=True) | |
| # Load the pre-trained model and vectorizer | |
| def load_model(): | |
| try: | |
| # Load TF-IDF Vectorizer | |
| with open('tfidf_vectorizer.pkl', 'rb') as vectorizer_file: | |
| tfidf_vectorizer = pickle.load(vectorizer_file) | |
| # Load KNN Classifier | |
| with open('knn_model.pkl', 'rb') as model_file: | |
| knn_classifier = pickle.load(model_file) | |
| return tfidf_vectorizer, knn_classifier | |
| except Exception as e: | |
| st.error(f"Error loading model: {e}") | |
| return None, None | |
| # Preprocess text function | |
| def preprocess_text(text): | |
| # Tokenization and preprocessing | |
| stop_words = set(stopwords.words('english')) | |
| words = word_tokenize(text.lower()) | |
| words = [word for word in words if word.isalpha() and word not in stop_words] | |
| return ' '.join(words) | |
| # Main Streamlit App | |
| def main(): | |
| # Set page title and favicon | |
| st.set_page_config( | |
| page_title="Disease Classification by Symptoms", | |
| page_icon=":medical_symbol:", | |
| layout="centered" | |
| ) | |
| # Title and description | |
| st.title("🩺 Disease Classification Predictor") | |
| st.markdown(""" | |
| ### Predict Potential Diseases Based on Symptoms | |
| Enter your symptoms below, and our AI model will help predict possible diseases. | |
| """) | |
| # Load model and vectorizer | |
| tfidf_vectorizer, knn_classifier = load_model() | |
| # Input form for symptoms | |
| with st.form(key='symptom_form'): | |
| symptoms = st.text_area( | |
| "Enter your symptoms:", | |
| placeholder="Example: low appetite, fever, headache", | |
| help="Provide a detailed description of your symptoms" | |
| ) | |
| submit_button = st.form_submit_button(label="Predict Disease") | |
| # Prediction logic | |
| if submit_button: | |
| if not symptoms: | |
| st.warning("Please enter some symptoms.") | |
| return | |
| try: | |
| # Preprocess input symptoms | |
| preprocessed_symptoms = preprocess_text(symptoms) | |
| # Transform symptoms using TF-IDF vectorizer | |
| symptoms_tfidf = tfidf_vectorizer.transform([preprocessed_symptoms]) | |
| # Predict disease | |
| predicted_disease = knn_classifier.predict(symptoms_tfidf) | |
| # Display prediction | |
| st.success(f"Predicted Disease: {predicted_disease[0]}") | |
| # Additional information (optional) | |
| st.info(""" | |
| ### Disclaimer | |
| - This is an AI-based prediction and should not replace professional medical advice | |
| - Always consult with a healthcare professional for accurate diagnosis | |
| - The prediction is based on machine learning analysis of symptom patterns | |
| """) | |
| except Exception as e: | |
| st.error(f"An error occurred during prediction: {e}") | |
| # Sidebar with additional information | |
| st.sidebar.title("About the Model") | |
| st.sidebar.markdown(""" | |
| ### Disease Classification Model | |
| - **Algorithm**: K-Nearest Neighbors (KNN) | |
| - **Features**: TF-IDF Vectorization | |
| - **Trained on**: Symptom to Disease Dataset | |
| #### How it works: | |
| 1. Transform symptoms into numerical features | |
| 2. Compare with known disease patterns | |
| 3. Predict most likely disease | |
| """) | |
| # Run the app | |
| if __name__ == "__main__": | |
| main() | |
| # Additional requirements.txt content | |
| """ | |
| streamlit | |
| scikit-learn | |
| nltk | |
| pickle5 | |
| """ | |
| # Deployment Notes | |
| """ | |
| Deployment Steps: | |
| 1. Install requirements: | |
| pip install -r requirements.txt | |
| 2. Download NLTK resources: | |
| python -m nltk.downloader punkt | |
| python -m nltk.downloader stopwords | |
| 3. Run the Streamlit app: | |
| streamlit run app.py | |
| """ |