Upload 6 files

lasso.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:758eee28caf0451b5771db86a5215b18fc6430919cc92063d9478a5d3543616c
+size 5195

linear.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ece41d276cae7e4f82d7f87a3176aac112169d4914978d7075602d4b37616ce
+size 857

main.py

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+import streamlit as st
+import pandas as pd
+import pickle
+
+
+st.set_page_config(
+    page_title="Air Quality Index prediction",
+    layout= "wide",
+    menu_items= {
+        "Get Help" : "https://github.com/kedarnathdev/AQIprediction",
+        "Report a bug" : "https://github.com/kedarnathdev/AQIprediction/issues",
+        "About": None,
+        # "About" : "Made by [@kedarnathdev](https://www.gitub.com/kedarnathdev)"
+    }
+)
+
+
+hide_streamlit_style = """
+            <style>
+            
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+
+
+models = {
+    'Random Forest': 'randomforest.pkl',
+    'SVM model': 'svm.pkl',
+    'Linear Regression': 'linear.pkl',
+    'Lasso Regression': 'lasso.pkl'
+}
+ 
+
+def load_model(filename):
+    with open(filename, 'rb') as model_file:
+        model = pickle.load(model_file)
+    return model
+
+st.header('Air Quality Index Predictor')
+st.subheader('Enter the following values:')
+T = st.number_input('Average Temperature')
+TM = st.number_input('Maximum Temperature')
+Tm = st.number_input('Minimum Temperature')
+SLP = st.number_input('Atmospheric pressure at sea level')
+H = st.number_input('Average Relative Humidity')
+VV = st.number_input('Average Visibility')
+V = st.number_input('Average Wind Speed')
+VM = st.number_input('Maximum Sustained Wind Speed')
+
+selected_model = st.selectbox('Select a model', list(models.keys()))
+
+if st.button('Predict'):
+    if not (T and TM and Tm and SLP and H and VV and V and VM):
+        st.error('Please fill all the fields.')
+    else:
+        model_file = models[selected_model]
+        model = load_model(model_file)
+
+        input_df = pd.DataFrame({
+            'T': T,
+            'TM': TM,
+            'Tm': Tm,
+            'SLP': SLP,
+            'H': H,
+            'VV': VV,
+            'V': V,
+            'VM': VM
+        }, index=[0])
+
+        prediction = model.predict(input_df)[0]
+
+        if prediction <= 50:
+            color = 'green'
+            description = 'Good: Air quality is satisfactory, and air pollution poses little or no risk.'
+        elif prediction <= 100:
+            color = 'yellow'
+            description = 'Moderate: Air quality is acceptable. However, there may be a risk for some people, particularly those who are unusually sensitive to air pollution.'
+        elif prediction <= 150:
+            color = 'orange'
+            description = 'Unhealthy for Sensitive Groups: Members of sensitive groups may experience health effects. The general public is less likely to be affected.'
+        elif prediction <= 200:
+            color = 'red'
+            description = 'Unhealthy: Some members of the general public may experience health effects members of sensitive groups may experience more serious health effects.'
+        elif prediction <= 300:
+            color = 'purple'
+            description = 'Very Unhealthy: Health alert, The risk of health effects is increased for everyone.'
+        else:
+            color = 'maroon'
+            description = 'Hazardous: Health warning of emergency conditions: everyone is more likely to be affected.'
+
+        
+        st.markdown(f'<h1 style="color:{color};">AQI: {prediction}</h1>', unsafe_allow_html=True)
+        st.write(description)

randomforest.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bac8cf4cfe26e180705f8f693f035c9b7f2593015234f5e3a0f6b3c5927aa276
+size 2513079

svm.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:79c131cfab25eaa0adcb7c449e69e56551508c0414707faa5d2c64fb5f2cce1e
+size 35583