Spaces:
Sleeping
Sleeping
File size: 3,476 Bytes
75d91b0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
import streamlit as st
import joblib
import re
import string
# Load the trained model and TF-IDF vectorizer
knn_model = joblib.load('knn_model.joblib')
tfidf_vectorizer = joblib.load('tfidf_vectorizer.joblib')
# Preprocess the input text (same preprocessing as in the notebook)
def preprocess_text(text):
text = text.lower() # Convert to lowercase
text = re.sub(r'\d+', '', text) # Remove digits
text = text.translate(str.maketrans('', '', string.punctuation)) # Remove punctuation
return text
# Prediction function
def predict_disease(symptom):
preprocessed_symptom = preprocess_text(symptom)
tfidf_features = tfidf_vectorizer.transform([preprocessed_symptom]).toarray()
predicted_disease = knn_model.predict(tfidf_features)
return predicted_disease[0]
# Streamlit UI Design
st.set_page_config(page_title="Disease Prediction App", page_icon="🦠", layout="centered")
# Custom Styling
st.markdown("""
<style>
body {
background-color: #2E2E2E;
color: white;
font-family: 'Segoe UI', sans-serif;
}
.header {
font-size: 36px;
font-weight: bold;
color: #00BFFF;
text-align: center;
margin-top: 30px;
margin-bottom: 15px;
}
.description {
font-size: 16px;
color: #dcdcdc;
text-align: center;
margin-bottom: 20px;
}
.input-box {
background-color: #3E3E3E;
border-radius: 8px;
padding: 15px;
font-size: 16px;
font-family: 'Segoe UI', sans-serif;
border: none;
color: white;
}
.output {
background-color: #5F5F5F;
border-radius: 8px;
padding: 15px;
font-size: 18px;
color: #00BFFF;
font-weight: bold;
text-align: center;
}
.btn {
background-color: #00BFFF;
color: white;
font-size: 18px;
padding: 12px 24px;
border-radius: 8px;
border: none;
cursor: pointer;
width: 100%;
}
.btn:hover {
background-color: #008B8B;
}
footer {
margin-top: 40px;
text-align: center;
font-size: 14px;
color: #dcdcdc;
}
.container {
padding: 20px;
border-radius: 12px;
background-color: #383838;
max-width: 500px;
margin: auto;
}
</style>
""", unsafe_allow_html=True)
# Title and Description
st.markdown("<div class='header'>🦠 Disease Prediction</div>", unsafe_allow_html=True)
st.markdown("<div class='description'>Enter your symptoms, and the model will predict the possible disease based on the provided input.</div>", unsafe_allow_html=True)
# Input Box and Prediction Button in a centered container
with st.container():
symptom = st.text_area("Enter symptoms:", height=150, max_chars=500, placeholder="E.g., fever, cough, headache...", key="symptom", label_visibility="collapsed")
if st.button("Predict", key="predict_button"):
if symptom:
predicted_disease = predict_disease(symptom)
st.markdown(f"<div class='output'>**Predicted Disease: {predicted_disease}**</div>", unsafe_allow_html=True)
else:
st.warning("Please enter some symptoms to predict the disease.", icon="⚠️")
# Footer with minimalistic text
st.markdown("""
<footer>
<p>Powered by AI | Developed for Final Year Project </p>
</footer>
""", unsafe_allow_html=True)
|