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| 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() | |