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