app.py

import streamlit as st
import pandas as pd
import joblib
import spacy
import subprocess

# Install spaCy model if not already installed
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    st.warning("Downloading spaCy model 'en_core_web_sm'... This may take a few minutes.")
    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
    nlp = spacy.load("en_core_web_sm")

# Load the trained model
model = joblib.load("disease_model.pkl")

# Load dataset for additional details
df = pd.read_csv("disease_dataset.csv")

def preprocess_input(symptoms):
    """Preprocess user input using spaCy."""
    doc = nlp(symptoms)
    tokens = [token.lemma_.lower() for token in doc if not token.is_stop and not token.is_punct]
    return " ".join(tokens)

def predict_disease(symptoms):
    """Predict disease based on symptoms."""
    processed_input = preprocess_input(symptoms)
    prediction = model.predict([processed_input])
    return prediction[0]

def get_disease_details(disease):
    """Get additional details about the disease."""
    details = df[df["disease"] == disease].iloc[0]
    return details

# Streamlit app
st.title("Medical Chatbot for Symptom Analysis")
st.write("Enter your symptoms below to get a possible diagnosis and recommendations.")

# User input
symptoms = st.text_input("Enter your symptoms (comma-separated):")

if st.button("Analyze Symptoms"):
    if symptoms:
        # Predict disease
        disease = predict_disease(symptoms)
        st.success(f"Predicted Disease: **{disease}**")

        # Get additional details
        details = get_disease_details(disease)
        st.subheader("Details:")
        st.write(f"**Cures:** {details['cures']}")
        st.write(f"**Recommended Doctor:** {details['doctor']}")
        st.write(f"**Risk Level:** {details['risk level']}")
    else:
        st.error("Please enter symptoms to analyze.")