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| import gradio as gr | |
| import numpy as np | |
| # If loading your actual model from HF: | |
| # from huggingface_hub import hf_hub_download | |
| # import joblib | |
| # model_path = hf_hub_download(repo_id="AroshN/priority_classif_xgb", filename="model.joblib") | |
| # model = joblib.load(model_path) | |
| def predict_priority(task_title, days_due, estimated_hours, urgency): | |
| # Dummy logic or model.predict(): | |
| # Replace this with your actual feature transformation & prediction | |
| score = (urgency * 2) + (estimated_hours / (days_due + 0.1)) | |
| if score > 8: | |
| priority = "🔴 High Priority" | |
| elif score > 4: | |
| priority = "🟡 Medium Priority" | |
| else: | |
| priority = "🟢 Low Priority" | |
| return { | |
| "Priority": priority, | |
| "Urgency Level": f"{urgency}/5", | |
| "Estimated Hours": f"{estimated_hours} hrs", | |
| "Due In": f"{days_due} days" | |
| } | |
| demo = gr.Interface( | |
| fn=predict_priority, | |
| inputs=[ | |
| gr.Textbox(label="Task Title / Description", placeholder="Fix payment gateway timeout bug"), | |
| gr.Slider(minimum=0, maximum=30, value=2, step=1, label="Days Until Due"), | |
| gr.Slider(minimum=0.5, maximum=40, value=4.0, step=0.5, label="Estimated Effort (Hours)"), | |
| gr.Radio(choices=[1, 2, 3, 4, 5], value=3, label="Urgency Rating (1=Low, 5=Critical)") | |
| ], | |
| outputs=gr.JSON(label="Prediction Result"), | |
| title="⚡ Task Priority Classification", | |
| description="Interactive ML demo predicting task priority using XGBoost." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |