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| import os | |
| import numpy as np | |
| import pandas as pd | |
| import gradio as gr | |
| import spaces | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn import metrics | |
| ### ------------------------------ ### | |
| ### Data Transformation ### | |
| ### ------------------------------ ### | |
| # Load dataset | |
| uncleaned_data = pd.read_csv('data.csv') | |
| # Remove timestamp from dataset (first column) | |
| uncleaned_data = uncleaned_data.iloc[:, 1:] | |
| data = pd.DataFrame() | |
| # Track categorical mappings | |
| cat_value_dicts = {} | |
| final_colname = uncleaned_data.columns[-1] | |
| for colname, colval in uncleaned_data.items(): | |
| if isinstance(colval.values[0], (np.integer, float, np.floating)): | |
| data[colname] = uncleaned_data[colname].copy() | |
| continue | |
| new_dict = {} | |
| val = 0 | |
| transformed_col_vals = [] | |
| for item in colval.values: | |
| if item not in new_dict: | |
| new_dict[item] = val | |
| val += 1 | |
| transformed_col_vals.append(new_dict[item]) | |
| if colname == final_colname: | |
| new_dict = {value: key for key, value in new_dict.items()} | |
| cat_value_dicts[colname] = new_dict | |
| data[colname] = transformed_col_vals | |
| ### -------------------------------- ### | |
| ### Model Training ### | |
| ### -------------------------------- ### | |
| feature_cols = [c for c in data.columns if c != final_colname] | |
| x = data[feature_cols] | |
| y = data[final_colname] | |
| x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42) | |
| # ZeroGPU decorator on startup model training | |
| def train_model(x_tr, y_tr): | |
| clf = LogisticRegression(max_iter=1000) | |
| clf.fit(x_tr, y_tr.values.ravel()) | |
| return clf | |
| model = train_model(x_train, y_train) | |
| y_pred = model.predict(x_test) | |
| acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + "%" | |
| def get_feat(): | |
| feats = [abs(val) for val in model.coef_[0]] | |
| max_val = max(feats) | |
| idx = feats.index(max_val) | |
| return feature_cols[idx] | |
| most_imp_feat = get_feat() | |
| ### -------------------------------- ### | |
| ### Helper: Read info.md ### | |
| ### -------------------------------- ### | |
| header_text = "Personalized Recommendation AI" | |
| body_text = "" | |
| if os.path.exists('info.md'): | |
| with open('info.md', 'r') as f: | |
| lines = f.readlines() | |
| if lines: | |
| header_text = lines[0].strip() | |
| body_text = "".join(lines[1:]) | |
| ### ------------------------------- ### | |
| ### Interface Creation ### | |
| ### ------------------------------- ### | |
| # ZeroGPU decorator on prediction function | |
| def general_predictor(*args): | |
| features = [] | |
| for colname, arg in zip(feature_cols, args): | |
| if colname in cat_value_dicts: | |
| features.append(cat_value_dicts[colname][arg]) | |
| else: | |
| features.append(float(arg) if arg is not None else 0.0) | |
| new_input = [features] | |
| result = model.predict(new_input)[0] | |
| if final_colname in cat_value_dicts: | |
| return cat_value_dicts[final_colname].get(result, str(result)) | |
| return str(result) | |
| with gr.Blocks() as demo: | |
| gr.Markdown(f"# {header_text}") | |
| gr.Markdown('Take the quiz to get a personalized recommendation using AI.') | |
| with gr.Row(): | |
| with gr.Column(): | |
| inputls = [] | |
| for colname in feature_cols: | |
| if colname in cat_value_dicts: | |
| radio_options = list(cat_value_dicts[colname].keys()) | |
| inputls.append(gr.Dropdown(radio_options, label=colname)) | |
| else: | |
| inputls.append(gr.Number(label=colname)) | |
| submit = gr.Button("Click to see your personalized result!", variant="primary") | |
| output = gr.Textbox(label="Your recommendation:", placeholder="your recommendation will appear here") | |
| submit.click(fn=general_predictor, inputs=inputls, outputs=output) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(f"### Accuracy:\n{acc}") | |
| with gr.Column(): | |
| gr.Markdown(f"### Most important feature:\n{most_imp_feat}") | |
| gr.Markdown( | |
| '⭐ *Note that model accuracy is based on the uploaded data.csv and reflects how well the AI model can give correct recommendations for that dataset.*' | |
| ) | |
| with gr.Column(): | |
| gr.Markdown(body_text) | |
| demo.launch() |