import streamlit as st
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
Load data for Ayurvedic medicine, hospitals, and colleges
def recommend_medicines(user_disease):
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(medicine_data['Diseases Cured'])
user_input_vector = tfidf_vectorizer.transform([user_disease])
cosine_similarities = cosine_similarity(user_input_vector, tfidf_matrix)
similar_indices = [i for i, score in enumerate(cosine_similarities[0]) if score > 0.5]
recommendations = []
for idx in similar_indices:
recommendation = {
"Ayurvedic Medicine": medicine_data.iloc[idx]['Ayurvedic Medicine'],
"Diseases Cured": medicine_data.iloc[idx]['Diseases Cured'],
"Cautions and Considerations": medicine_data.iloc[idx]['Cautions and Precautions'],
"Properties": medicine_data.iloc[idx]['Properties'],
"Key Ingredients": medicine_data.iloc[idx]['Key Ingredients'],
"Mode of Action": medicine_data.iloc[idx]['Mode of Action']
}
recommendations.append(recommendation)
return recommendations
Function to recommend Ayurvedic Hospitals
def recommend_hospitals(user_state):
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(hospital_data['State'])
user_vector = vectorizer.transform([user_state])
similarity = cosine_similarity(user_vector, tfidf_matrix)
similar_indices = [i for i, score in enumerate(similarity[0]) if score > 0.75]
recommendations = []
for idx in similar_indices:
recommendation = {
"Name": hospital_data.iloc[idx]['Name'],
"Address": hospital_data.iloc[idx]['Address']
}
recommendations.append(recommendation)
return recommendations
Function to recommend Ayurvedic Colleges
def recommend_colleges(user_state):
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(college_data['State'])
user_matrix = vectorizer.transform([user_state])
similarity = cosine_similarity(user_matrix, tfidf_matrix)
similar_indices = [i for i, score in enumerate(similarity[0]) if score > 0.5]
recommendations = []
for idx in similar_indices:
recommendation = {
"College ID": college_data.iloc[idx]['College ID'],
"Name of the College": college_data.iloc[idx]['Name of the College'],
"State": college_data.iloc[idx]['State']
}
recommendations.append(recommendation)
return recommendations
if section == "Ayurvedic Medicine":
user_disease = st.text_input("Enter the disease name:")
if st.button("Recommend Medicines"):
st.subheader("Recommended Medicines:")
recommendations = recommend_medicines(user_disease)
for recommendation in recommendations:
st.write(recommendation)
elif section == "Ayurvedic Hospitals":
user_state = st.text_input("Enter the name of your state:")
if st.button("Recommend Hospitals"):
st.subheader("Recommended Hospitals:")
recommendations = recommend_hospitals(user_state)
for recommendation in recommendations:
st.write(recommendation)
elif section == "Ayurvedic Colleges":
user_state = st.text_input("Enter your state name:")
if st.button("Recommend Colleges"):
st.subheader("Recommended Colleges:")
recommendations = recommend_colleges(user_state)
for recommendation in recommendations:
st.write(recommendation)
