import streamlit as st
import re
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split

# Load your symptom-disease data
data = pd.read_csv("Symptom2Disease.csv")

# Initialize the TF-IDF vectorizer
tfidf_vectorizer = TfidfVectorizer()

# Apply TF-IDF vectorization to the preprocessed text data
X = tfidf_vectorizer.fit_transform(data['text'])

# Split the dataset into a training set and a testing set
X_train, X_test, y_train, y_test = train_test_split(X, data['label'], test_size=0.2, random_state=42)

# Initialize the Multinomial Naive Bayes model
model = MultinomialNB()

# Train the model on the training data
model.fit(X_train, y_train)

# Set Streamlit app title with emojis
st.title("Health Symptom-to-Disease Predictor 🏥👨‍⚕️")

# Define a sidebar
st.sidebar.title("Tool Definition")
st.sidebar.markdown("This tool helps you identify possible diseases based on the symptoms you provide.")
st.sidebar.markdown("the tool may aid healthcare professionals in the initial assessment of potential conditions, facilitating quicker decision-making and improving patient care")

st.sidebar.title("⚠️ **Limitation**")
st.sidebar.markdown("This tool's predictions are based solely on symptom descriptions and may not account for other critical factors,")
st.sidebar.markdown("such as a patient's medical history or laboratory tests,")
st.sidebar.markdown("As such,it should be used as an initial reference and not as a sole diagnostic tool. 👩‍⚕️")
                    
st.warning("Please note that this tool is for informational purposes only. Always consult a healthcare professional for accurate medical advice.")
show_faqs = st.sidebar.checkbox("Show FAQs")

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Function to preprocess user input
def preprocess_input(user_input):
    user_input = user_input.lower()  # Convert to lowercase
    user_input = re.sub(r"[^a-zA-Z\s]", "", user_input)  # Remove special characters and numbers
    user_input = " ".join(user_input.split())  # Remove extra spaces
    return user_input

# Function to predict diseases based on user input
def predict_diseases(user_clean_text):
    user_input_vector = tfidf_vectorizer.transform([user_clean_text])  # Vectorize the cleaned user input
    predictions = model.predict(user_input_vector)  # Make predictions using the trained model
    return predictions

# Add user input section
user_input = st.text_area("Enter your symptoms (how you feel):", key="user_input")

# Add button to predict disease
if st.button("Predict Disease"):
    # Display loading message
    with st.spinner("Diagnosing patient..."):
        # Check if user input is not empty
        if user_input:
            cleaned_input = preprocess_input(user_input)
            predicted_diseases = predict_diseases(cleaned_input)

            # Display predicted diseases
            st.session_state.messages.append({"role": "user", "content": user_input})
            st.session_state.messages.append({"role": "assistant", "content": f"Based on your symptoms, you might have {', '.join(predicted_diseases)}."})

            st.write("Based on your symptoms, you might have:")
            for disease in predicted_diseases:
                st.write(f"- {disease}")
        else:
            st.warning("Please enter your symptoms before predicting.")

# Create FAQs section
if show_faqs:
    st.markdown("## Frequently Asked Questions")
    st.markdown("**Q: How does this tool work?**")
    st.markdown("A: The tool uses a machine learning model to analyze the symptoms you enter and predicts possible diseases based on a pre-trained dataset.")

    st.markdown("**Q: Is this a substitute for a doctor's advice?**")
    st.markdown("A: No, this tool is for informational purposes only. It's essential to consult a healthcare professional for accurate medical advice.")

    st.markdown("**Q: Can I trust the predictions?**")
    st.markdown("A: While the tool provides predictions, it's not a guarantee of accuracy. It's always best to consult a healthcare expert for a reliable diagnosis.")

# Add attribution
st.markdown("Created with ❤️ by Joas")