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] # rfc.predict() gives data in a nested list. So selecting list at 0th index
    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 f"{class_name}\n\nThank You For Using Our Model 😊 !!"

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",
    title="<span id=title-first>Aqua</span> <span id=title-second>💧Alert</span> <br> "
          "<span id=title-desc> a ML Model which can predict contaminants present in the waste water</span>",

    description="<span id=desc-info> <br>★ Some examples are present at the bottom for your convenience <a id=a-tag "
                "href=https://theabhishek.me/Aqua-Alert/ target=_blank>(visit website)</a> </span>",
    theme=gr.themes.Soft(),
    css="""
        .gradio-container {background-color: #daeefe}
        #tbox { background-color : teal !important; }
        #tbox textarea {background-color: black; font-size : 15px; color : white; font-weight : bold; !important;}
         
        #title-first {color:#034782; font-size : 50px; border-style: solid; border-color: black  !important;}
        #title-second {color:#034782; font-size : 50px !important;}
        #title-desc {color : black; text-align: center; font-size : 15px !important;}
        #title-desc:hover {background-color : yellow !important;}
        
        #desc-info {font-size : 12px; color: black; font-weight : bold; text-align: center !important;}
        
        #a-tag { color : black !important;}
        #a-tag:hover { text-decoration : none !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],
        [1506, 12770, 17221, -10802, 2017,-1800, 7413,-2530, 5279, -2600, 3073,-3492 ],
        [3044, 15856, 20114, -5594, 6915, -2732, 15199, -2292, 14491, -2392, 16643, -1362]
    ]
)

demo.launch(inline=False)