import gradio as gr

# Hardcoded NEET cutoff data for top 10 medical colleges in India (example data) by reservation category
colleges_data = {
    "All India Institute of Medical Sciences (AIIMS) Delhi": {
        "GEN": [705, 700, 705],
        "OBC": [685, 680, 685],
        "SC": [675, 670, 675],
        "ST": [670, 665, 670],
    },
    "Maulana Azad Medical College (MAMC) Delhi": {
        "GEN": [690, 685, 690],
        "OBC": [675, 670, 675],
        "SC": [665, 660, 665],
        "ST": [660, 655, 660],
    },
    "Christian Medical College (CMC) Vellore": {
        "GEN": [675, 670, 675],
        "OBC": [660, 655, 660],
        "SC": [650, 645, 650],
        "ST": [645, 640, 645],
    },
    "King George's Medical University (KGMU) Lucknow": {
        "GEN": [665, 660, 665],
        "OBC": [650, 645, 650],
        "SC": [640, 635, 640],
        "ST": [635, 630, 635],
    },
    "Jawaharlal Institute of Postgraduate Medical Education & Research (JIPMER) Puducherry": {
        "GEN": [670, 665, 670],
        "OBC": [655, 650, 655],
        "SC": [645, 640, 645],
        "ST": [640, 635, 640],
    },
    "Grant Medical College Mumbai": {
        "GEN": [655, 650, 655],
        "OBC": [640, 635, 640],
        "SC": [630, 625, 630],
        "ST": [625, 620, 625],
    },
    "Seth GS Medical College Mumbai": {
        "GEN": [660, 655, 660],
        "OBC": [645, 640, 645],
        "SC": [635, 630, 635],
        "ST": [630, 625, 630],
    },
    "Banaras Hindu University (BHU) Varanasi": {
        "GEN": [675, 670, 675],
        "OBC": [660, 655, 660],
        "SC": [650, 645, 650],
        "ST": [645, 640, 645],
    },
    "Lady Hardinge Medical College (LHMC) Delhi": {
        "GEN": [680, 675, 680],
        "OBC": [665, 660, 665],
        "SC": [655, 650, 655],
        "ST": [650, 645, 650],
    },
    "University College of Medical Sciences (UCMS) Delhi": {
        "GEN": [685, 680, 685],
        "OBC": [670, 665, 670],
        "SC": [660, 655, 660],
        "ST": [655, 650, 655],
    }
}

# Function to calculate eligible colleges based on NEET score and reservation category
def neet_cutoff_calculator(score, category):
    eligible_colleges = []
    for college, cutoffs in colleges_data.items():
        average_cutoff = sum(cutoffs[category]) / len(cutoffs[category])
        if score >= average_cutoff:
            eligible_colleges.append(f"{college} (Avg Cutoff: {average_cutoff:.2f})")
    return eligible_colleges

# Function for Gradio interface
def calculate_colleges(score, category):
    eligible_colleges = neet_cutoff_calculator(score, category)
    if eligible_colleges:
        return f"With a score of {score}, you are eligible for admission to the following colleges: {', '.join(eligible_colleges)}"
    else:
        return "Unfortunately, no colleges match your score for the selected category."

# Create the Gradio interface using the updated syntax
iface = gr.Interface(
    fn=calculate_colleges,
    inputs=[
        gr.Slider(0, 720, label="NEET Score"),
        gr.Dropdown(["GEN", "OBC", "SC", "ST"], label="Category")
    ],
    outputs="text",
    title="NEET Cut-Off Calculator by Category"
)

# Launch the interface
iface.launch()