from typing import List, Tuple import gradio as gr import os import sys if os.environ.get("DEV_MODE"): # for local development sys.path.insert(0, os.path.abspath("../fsrs-optimizer/src/fsrs_optimizer/")) from fsrs_optimizer import Optimizer, DEFAULT_WEIGHT, FSRS, lineToTensor def interface_func( weights: str, ratings: str, delta_ts: str, request_retention: float ) -> str: weights = weights.replace("[", "").replace("]", "") optimizer = Optimizer() optimizer.w = list(map(lambda x: float(x.strip()), weights.split(","))) test_sequence = optimizer.preview_sequence( ratings.replace(" ", ""), request_retention ) default_preview = optimizer.preview(request_retention) if delta_ts != "": s_history, d_history = memory_state_sequence(ratings, delta_ts, optimizer.w) return ( test_sequence, default_preview, f"s: {(', '.join(s_history))}\nd: {', '.join(d_history)}", ) return test_sequence, default_preview, "" def memory_state_sequence( r_history: str, t_history: str, weights: List[float] ) -> Tuple[List[float], List[float]]: fsrs = FSRS(weights) line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1) outputs, _ = fsrs(line_tensor) stabilities, difficulties = outputs.transpose(0, 1)[0].transpose(0, 1) return map(lambda x: str(round(x, 2)), stabilities.tolist()), map( lambda x: str(round(x, 2)), difficulties.tolist() ) iface = gr.Interface( fn=interface_func, inputs=[ gr.Textbox( label="weights", lines=1, value=str(DEFAULT_WEIGHT)[1:-1], ), gr.Textbox(label="ratings", lines=1, value="3,3,3,3,1,3,3"), gr.Textbox(label="delta_ts (requried by state history)", lines=1, value=""), gr.Slider( label="Your Request Retention", minimum=0.6, maximum=0.97, step=0.01, value=0.9, ), ], outputs=[ gr.Textbox(label="test sequences"), gr.Textbox(label="default preview"), gr.Textbox(label="state history (require delta_ts)"), ], ) iface.launch()