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import gradio as gr | |
import pandas as pd | |
from sklearn.model_selection import train_test_split | |
from sklearn.ensemble import RandomForestClassifier | |
#Loading the dataset | |
df = pd.read_csv('data/one_data.csv') | |
x_train, x_test, y_train, y_test = train_test_split(df.drop('CLASS', axis=1), df['CLASS'], test_size=0.2, random_state=42) | |
# Create a random forest classifier | |
rfc = RandomForestClassifier() | |
# Training the model using the training data | |
rfc.fit(x_train, y_train) | |
# Predicting the Loan Approval Status | |
#rfc_pred = rfc.predict(x_test) | |
def ml_model(a, b, c, d, e, f, g, h, i, j, k, l): | |
test_data = [[a, b, c, d, e, f, g, h, i, j, k, l]] | |
rfc_pred = rfc.predict(test_data)[0] | |
dict = { | |
0: "Acetic Acid", | |
1: "Acetone", | |
2: "Ammonia", | |
3: "Ethanol", | |
4: "Formic Acid", | |
5: "Hydrochloric Acid", | |
6: "Hydrogen Peroxide", | |
7: "Phosphoric Acid", | |
8: "Sodium Hypochlorite", | |
9: "Sulphuric Acid", | |
10: "Waste water" | |
} | |
class_name = f"Class : {dict[rfc_pred]}" | |
return class_name, "Thank You" | |
demo = gr.Interface( | |
fn=ml_model, | |
inputs=[ | |
gr.Textbox(label="PLATINUM_78kHz_RESISTANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="GOLD_78kHz_RESISTANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="PLATINUM_200Hz_RESISTANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="PLATINUM_200Hz_CAPACITANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="GOLD_200Hz_RESISTANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="GOLD_200Hz_CAPACITANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="COPPER_200Hz_RESISTANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="COPPER_200Hz_CAPACITANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="SILVER_200Hz_RESISTANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="SILVER_200Hz_CAPACITANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="NICKEL_200Hz_RESISTANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
gr.Textbox(label="NICKEL_200Hz_CAPACITANCE", placeholder="Enter data here..", elem_id="tbox", type='text'), | |
], | |
outputs=["text", "text"], | |
title="Aqua Alert", | |
description="<strong><center> a ML Model which can predict contaminants present " | |
"in the waste water</center> </strong>", | |
theme=gr.themes.Soft(), | |
css=""" | |
#tbox { color : black; width: 100 !important; } | |
#tbox textarea {background-color: white; font-size : 15px; color : black; | |
font-weight : bold; !important;} | |
""", | |
examples=[ | |
[2292.000, 13980.000, 1206.0000, -523.000, 4698.000, -3925.0000, 6757.0000, -3263.0000, 11269.000, -3596.000, | |
14611.000, -1492.000], | |
[3506, 16706, 22694, -4568, 14771, -2786, 15861, -1989, 15082, -2095, 17293, -133], | |
[3230, 16109, 19895, -6970, 7621, -3032, 14481, -2957, 13919, -3001, 16518, -2134] | |
] | |
) | |
demo.launch(inbrowser=True) | |