feat: add almanac checklist generator with Gradio UI

This initial commit introduces a web application for generating an almanac checklist based on the 'herbologist_almanac_data.json' file. The app uses Gradio for a user-friendly interface and allows users to generate a checklist image and a recovery token for their plants.

Implements functionality to parse the token and update checklist inputs.
Provides a mechanism to generate an image from the selected checklist data.
Includes error handling for base64 token conversion.

.gitignore

@@ -0,0 +1,5 @@
+data.csv
+herbologist_almanac_data.json
+number.py
+test_app.py
+*.pyc

app.py

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+import base64
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from PIL import Image
+from io import BytesIO
+
+df = pd.read_csv("herbologist_almanac_checklist_data.csv")
+columns_with_tasks = [
+    "task1",
+    "task2",
+    "task3",
+    "task4",
+    "color1",
+    "color2",
+    "color3",
+    "color4",
+    "color5",
+    "color6",
+]
+PLANTS = list(df["plant"].unique())
+# 更改字体设置
+# rc("font", family="Arial Unicode MS")
+
+# def decimal2base64(decimal_int):
+#     # 定义 64 进制字符集
+#     base64_chars = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
+
+#     if decimal_int == 0:
+#         return "0"
+
+#     base64_str = ""
+#     while decimal_int > 0:
+#         remainder = decimal_int % 64
+#         base64_str = base64_chars[remainder] + base64_str
+#         decimal_int //= 64
+
+#     return base64_str
+
+
+def bin_ls2base64(ls):
+    # 将二进制列表转换为二进制列表
+    binary_str = "".join(str(n) for n in ls)
+
+    # decimal_int = int(bin_str, 2)
+    byte_str = int(binary_str, 2).to_bytes((len(binary_str) + 7) // 8, byteorder="big")
+    base64_str = base64.b64encode(byte_str).decode("utf-8")
+    return base64_str
+
+
+def base64_to_binary(base64_str):
+    if isinstance(base64_str, str):
+        # 将base64字符串转换为二进制列表
+        byte_str = base64.b64decode(base64_str)
+        binary_str = bin(int.from_bytes(byte_str, byteorder="big"))[2:].zfill(
+            len(columns_with_tasks) * len(PLANTS)
+        )
+        # ls = [int(n) for n in binary_str]
+        return binary_str
+
+
+def parse_token(token):
+    try:
+        if not token:  # 处理空字符串的情况
+            token = "\x00"
+
+        if len(token) > 0:
+            payload = base64_to_binary(token)
+            almanac_data: list = [int(n) for n in payload]
+            # print(len(almanac_data))
+            parsed_dict = {}
+            for _, row in df.iterrows():
+                parsed_dict[row["plant"]] = [
+                    almanac_data.pop() for _ in range(len(columns_with_tasks))
+                ]
+
+        return parsed_dict
+    except Exception as e:
+        print(e)
+
+
+# 定义一个简单的函数，模拟接收DataFrame数据
+def process_data(*args):
+    almanac_dict = dict(zip(PLANTS, args))
+
+    almanac_df = df.filter(items=["plant", "name"] + columns_with_tasks)
+    almanac_bin_ls = []
+    for pl in PLANTS:
+        almanac_inp = almanac_dict[pl]
+
+        almanac_bin = [0 for _ in range(len(columns_with_tasks))]
+
+        for n in almanac_inp:
+            almanac_bin[n] = 1
+
+        almanac_bin_ls += almanac_bin
+
+        for _, i in enumerate(almanac_inp):
+            almanac_df.loc[almanac_df["plant"] == pl, columns_with_tasks[i]] = "✔"
+    almanac_reverse_64 = bin_ls2base64(reversed(almanac_bin_ls))
+    # 这里可以添加处理DataFrame的逻辑
+    return (
+        generate_img(almanac_df.drop(columns=df.columns[0])),
+        almanac_reverse_64,
+    )
+
+
+def show_checkbox_groups(token):
+    checklist_inputs = []
+    parsed_dict = parse_token(token)
+    for index, row in df.iterrows():
+        tasks = [
+            row[col][0].upper() + row[col][1:]
+            for col in columns_with_tasks
+            if df.notnull().at[index, col]
+        ]
+        with gr.Row():
+            checkbox = gr.CheckboxGroup(
+                tasks,
+                label=f"{row['name']}",
+                value=[
+                    tasks[i]
+                    for i, v in enumerate(parsed_dict[row["plant"]])
+                    if v == 1 and df.notnull().at[index, columns_with_tasks[i]]
+                ],
+                type="index",
+            )
+            checklist_inputs.append(checkbox)
+
+    return checklist_inputs
+
+
+def wrap_text(text, max_width=20):
+    """Manually wrap text based on max_width (character count)"""
+    wrapped_lines = []
+    words = text.split(" ")
+    line = ""
+
+    for word in words:
+        if len(line) + len(word) + 1 <= max_width:
+            line += word + " "
+        else:
+            wrapped_lines.append(line.strip())
+            line = word + " "
+
+    wrapped_lines.append(line.strip())
+    return "\n".join(wrapped_lines)
+
+
+def generate_img(df):
+    fig, ax = plt.subplots(
+        figsize=(12, 16)
+    )  # Adjust the figure size for better readability
+    ax.axis("off")  # Turn off the axis
+
+    # Create a table with a light grey background for the header
+    table = ax.table(
+        cellText=df.values,
+        colLabels=df.columns,
+        loc="center",
+        colColours=["#f2f2f2"] * len(df.columns),
+        colWidths=[w / len(df.columns) for w in [1] + [2] * 4 + [1] * 6],
+    )
+
+    table.auto_set_font_size(False)
+    table.set_fontsize(10)
+
+    # Apply text wrapping and center alignment to non-header cells
+    for (row, col), cell in table.get_celld().items():
+
+        if cell.get_text().get_text() == "nan":
+            cell.set_text_props(text="", ha="center")
+        if row == 0:
+            cell.set_text_props(
+                weight="bold", ha="center"
+            )  # Bold and center-align header text
+        if col == 0:
+            text = cell.get_text().get_text()
+            wrapped_text = wrap_text(text, 9)
+            cell.set_text_props(text=wrapped_text, ha="center", linespacing=1)
+        else:
+            text = cell.get_text().get_text()
+            wrapped_text = wrap_text(text)
+            cell.set_text_props(
+                text=wrapped_text,
+                ha="center",
+                fontsize=10,
+                linespacing=1,
+            )  # Enable text wrapping and center-align
+
+    # Manually scale table if needed for better readability with wrapped text
+    table.scale(1, 4)  # Adjust row height
+
+    # Ensure the layout is adjusted properly
+    plt.tight_layout(pad=0.5)  # Increase padding slightly
+
+    # Save the figure to a bytes buffer
+    buffer = BytesIO()
+    fig.savefig(
+        buffer, format="png", pad_inches=0.2
+    )  # Adjust padding around the figure
+    plt.close(fig)  # Close the figure to free up memory
+    buffer.seek(0)
+    image = Image.open(buffer)
+
+    return image
+
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        """
+        <center><font size=8>👩🏻‍🌾Herbology Almanac Checklist Generator📝</font></center>
+        This is a simple web app that generates an almanac checklist for your plants.
+        """
+    )
+    recovery_token = gr.Textbox(
+        value="",
+        label="Recovery Token",
+        info="Save this token or paste your saved one here",
+        placeholder="Keep this token to restore your previous input".upper(),
+        interactive=True,
+    )
+
+    checklist_inputs = show_checkbox_groups(recovery_token.value)
+
+    submit_button = gr.Button("Generate Image and Token")
+
+    # df_out = gr.Dataframe(label="Output Dataframe", interactive=False)
+
+    generated_img = gr.Image(label="Generated Image", format="png", type="pil")
+
+    submit_button.click(
+        process_data,
+        inputs=checklist_inputs,
+        outputs=[generated_img, recovery_token],
+    )
+
+    recovery_token.change(
+        show_checkbox_groups,
+        inputs=[recovery_token],
+        outputs=checklist_inputs,
+    )
+
+    # generate_button.click(
+    #     generate_img,
+    #     inputs=[df_out],
+    #     outputs=[generated_img],
+    # )
+
+
+app.launch()

herbologist_almanac_checklist_data.csv

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+plant,name,sort,HARVEST_score>100,task1,status1,task2,status2,task3,status3,task4,status4,color1,color2,color3,color4,color5,color6,count,done_or_not
+fanged_geranium,Fanged Geranium,1,False,Provide 100 MOISTURE points,False,SELL 5,False,HARVEST 10 Radiant,False,HARVEST 100,False,,,,,,,5,no
+gillyweed,Gillyweed,2,False,HARVEST 10,False,"Provide 5,000 HYGIENE points",False,HARVEST 10 in the black Lake,False,SELL 25 Radiant,False,,,,,,,5,no
+rose,Rose,3,False,HARVEST 5 in non-default color,False,SELL 10 Flourishing or above,False,COLLECT 10 starters,False,HARVEST 30 Flourishing or above,False,red,yellow,pink,blue,white,black,11,no
+puffapod,Puffapod,4,False,HARVEST 1 with MOISTURE never reaching 0,False,HARVEST 5,False,HARVEST 10 Radiant,False,"Provide 30,000 MOISTURE points",False,,,,,,,5,no
+wild_pansy,Wild Pansy,4,False,Remove 10 PESTS,False,Show off 1 rare-colored,False,HARVEST 30 Radiant,False,SELL 10 rare-colored,False,turquoise,yellow,orange,pink,white,purple,11,no
+nifflers_fancy,Niffler's Fancy,5,False,COLLECT 1 starter,False,Make 100 gems from SELLing,False,SELL 5 Radiant,False,HARVEST 15 Radiant,False,,,,,,,5,no
+fanwort,Fanwort,6,False,HARVEST 45 never damaged by PESTS,False,"Accumulate 10,000 LIGHT points",False,SELL 150 Radiant,False,HARVEST 100 in non-default colors,False,chartreuse,orange,red,turquoise,purple,white,11,no
+ladys_mantle,Lady's Mantle,6,False,HARVEST 1 special color,False,HARVEST 5 Flourishing or above,False,HARVEST 3 special color,False,"Accumulate 80,000 LIGHT points",False,yellow,white,orange,turquoise,viridian,purple,11,no
+mandrake,Mandrake,7,False,Remove 50 PESTS,False,Show off 1 Radiant,False,SELL 200 Flourishing or above,False,HARVEST 500,False,,,,,,,5,no
+kelp,Kelp,7,False,Remove 30 PESTS,False,"Provide 5,000 MOOD points",False,HARVEST 150 in the black Lake,False,SELL 300 Radiant,False,chartreuse,turquoise,orange,purple,pink,red,11,no
+cabbage,Cabbage,8,False,HARVEST 5 with MOOD over 60,False,"Provide 50,000 MOOD points",False,SELL 100 Radiant,False,"HARVEST 1,000",False,,,,,,,5,no
+peony,Peony,9,False,HARVEST 45 with its MOISTURE never reaching 0,False,SELL 60 Radiant ,False,HARVEST 100 of Flourishing or above,False,"Provide 70,000 LIGHT points",False,chartreuse,orange,white,red,pink,purple,11,no
+dragons_breath_macroalgae,Dragon's Breath,9,False,HARVEST 50 with HYGIENE never reaching 0,False,HARVEST 150 never damaged by PESTS,False,"Accumulate 30,000 LIGHT points",False,HARVEST 80 in non-default color,False,red,viridian,purple,pink,white,yellow,11,no
+begonia,Begonia,10,False,HARVEST 50 in non-default color,False,SELL 100 Radiant,False,HARVEST 200 rare-colored,False,"Provide 35,000 MOISTURE points",False,pink,red,yellow,orange,purple,white,11,no
+mayflower,Mayflower,10,False,HARVEST 100 Flourishing or above,False,"Accumulate 50,000 LIGHT points",False,SELL 100 Radiant,False,HARVEST 150 in non-default color,False,red,purple,turquoise,pink,white,blue,11,no
+hydrangea,Hydrangea,11,False,Show off 1 Radiant,False,Remove 50 PESTS,False,SELL 100 Radiant,False,HARVEST 180 rare-colored,False,orange,yellow,white,viridian,pink,purple,1 of 11,no
+ludwigia_glandulosa,Ludwigia,11,False,"HARVEST 10 with MOOD always above 60",False,HARVEST 50 growing up in a clean environment,False,"Accumulate 20,000 LIGHT points",False,SELL 30 Radiant,False,yellow,turquoise,red,pink,black,purple,11,
+daffodil,Daffodil,12,False,Remove 50 PESTS,False,"Accumulate 100,000 LIGHT points",False,HARVEST 450 never damaged by PESTS,False,SELL 600 Radiant,False,yellow,orange,viridian,purple,white,pink,11,no
+water_hyacinth,Hyacinth,13,False,GATHER 45 times during open world exploration,False,HARVEST 100 never invaded by OVERGROWTHS,False,HARVEST 180 rare-colored,False,SELL 50,False,white,pink,purple,black,orange,blue,11,
+lily_of_the_valley,Lily of the Valley,14,False,GATHER 35 times during open world exploration,False,"Accumulate 50,000 LIGHT points",False,HARVEST 100 never invaded by OVERGROWTHS,False,SELL 100 Radiant,False,white,blue,yellow,chartreuse,purple,pink,11,
+mosaic_flower,Mosaic Flower,15,False,HARVEST 100 with HYGIENE never reaching 0,False,SELL 50 Radiant,False,HARVEST 100 never invaded by OVERGROWTHS,False,HARVEST 200,False,yellow,orange,chartreuse,white,pink,blue,11,
+sunflower,Sunflower,16,False,HARVEST 120 rare-colored,False,GATHER 50 times during open world exploration,False,Make 150 gems from SELLing,False,HARVEST 45 never invaded by OVERGROWTHS,False,orange,yellow,white,red,blue,purple,11,
+mimbulus_mimbletonia,Mimbulus,17,False,Show off Radiant 80 times,False,HARVEST 100 Safe and Sound,False,"Provide 50,000 MOOD points",False,HARVEST 100 Radiant,False,,,,,,,5,
+water_lily,Water Lily,18,False,Unknown,False,Unknown,False,Unknown,False,Unknown,False,yellow,pink,white,purple,blue,black,11,

pyproject.toml

@@ -0,0 +1,16 @@
+[tool.poetry]
+name = "hpma-herbologist-almanac-checklist"
+version = "0.1.0"
+description = ""
+authors = ["Admin <channingwang2018@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.12"
+gradio = "4.31.3"
+uvicorn = "^0.30.6"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

@@ -0,0 +1,1101 @@
+aiofiles==23.2.1 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107 \
+    --hash=sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a
+altair==5.4.1 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:0ce8c2e66546cb327e5f2d7572ec0e7c6feece816203215613962f0ec1d76a82 \
+    --hash=sha256:0fb130b8297a569d08991fb6fe763582e7569f8a04643bbd9212436e3be04aef
+annotated-types==0.7.0 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \
+    --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89
+anyio==4.4.0 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94 \
+    --hash=sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7
+attrs==24.2.0 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346 \
+    --hash=sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2
+certifi==2024.8.30 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8 \
+    --hash=sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9
+charset-normalizer==3.3.2 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027 \
+    --hash=sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087 \
+    --hash=sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786 \
+    --hash=sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8 \
+    --hash=sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09 \
+    --hash=sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185 \
+    --hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \
+    --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
+    --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \
+    --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \
+    --hash=sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269 \
+    --hash=sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3 \
+    --hash=sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f \
+    --hash=sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6 \
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+    --hash=sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d \
+    --hash=sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e \
+    --hash=sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6 \
+    --hash=sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269 \
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+    --hash=sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d \
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+    --hash=sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed \
+    --hash=sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068 \
+    --hash=sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac \
+    --hash=sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25 \
+    --hash=sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8 \
+    --hash=sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab \
+    --hash=sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26 \
+    --hash=sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2 \
+    --hash=sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db \
+    --hash=sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f \
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+    --hash=sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28 \
+    --hash=sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de
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+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
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+    --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \
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+fastapi==0.112.2 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:3d4729c038414d5193840706907a41839d839523da6ed0c2811f1168cac1798c \
+    --hash=sha256:db84b470bd0e2b1075942231e90e3577e12a903c4dc8696f0d206a7904a7af1c
+ffmpy==0.4.0 ; python_version >= "3.12" and python_version < "4.0" \
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+filelock==3.15.4 ; python_version >= "3.12" and python_version < "4.0" \
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+    --hash=sha256:e5b708073ea3d684235648786f5f6153a48dc8762cdfe5563c57e80787c29fbb \
+    --hash=sha256:e8bf06b94694251861ba7fdeea15c8ec0967f84c3d4143ae9daf42bbc7717fe3 \
+    --hash=sha256:f08df60fbd8d289152079a65da4e66a447efc1d5d5a4d3f299cdd39e3b2e4a7d \
+    --hash=sha256:f1f8758a2ad110bd6432203a344269f445a2907dc24ef6bccfd0ac4e14e0d71d \
+    --hash=sha256:f677ce218976496a587ab17140da141557beb91d2a5c1a14212c994093f2eae2
+fsspec==2024.6.1 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:3cb443f8bcd2efb31295a5b9fdb02aee81d8452c80d28f97a6d0959e6cee101e \
+    --hash=sha256:fad7d7e209dd4c1208e3bbfda706620e0da5142bebbd9c384afb95b07e798e49
+gradio-client==0.16.3 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:e35a9b36e67fe57fce932aa08f759c3ec6c8a7fa9faee3b169d74a2acc347454 \
+    --hash=sha256:faba47cfd3c742bf7e454166ecd7e82de09a77c5798e35f1ffa97b2af8747dab
+gradio==4.31.3 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:5b98caaf5515614e0caca0c54e932a66039b7af9e980621331472b7bfacda9ca \
+    --hash=sha256:e1c62551d30861a456c4122e3809b09129a7587e18c08305d2eb8c3e789eca38
+h11==0.14.0 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d \
+    --hash=sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761
+httpcore==1.0.5 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61 \
+    --hash=sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5
+httpx==0.27.2 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0 \
+    --hash=sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2
+huggingface-hub==0.24.6 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:a990f3232aa985fe749bc9474060cbad75e8b2f115f6665a9fda5b9c97818970 \
+    --hash=sha256:cc2579e761d070713eaa9c323e3debe39d5b464ae3a7261c39a9195b27bb8000
+idna==3.8 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac \
+    --hash=sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603
+importlib-resources==6.4.4 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:20600c8b7361938dc0bb2d5ec0297802e575df486f5a544fa414da65e13721f7 \
+    --hash=sha256:dda242603d1c9cd836c3368b1174ed74cb4049ecd209e7a1a0104620c18c5c11
+jinja2==3.1.4 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369 \
+    --hash=sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d
+jsonschema-specifications==2023.12.1 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc \
+    --hash=sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c
+jsonschema==4.23.0 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4 \
+    --hash=sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566
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+markdown-it-py==3.0.0 ; python_version >= "3.12" and python_version < "4.0" and sys_platform != "emscripten" \
+    --hash=sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 \
+    --hash=sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb
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+    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
+    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
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+    --hash=sha256:d352ee8ac1926d6193f602cbe36b1643bbd1bbcb25e3c1a657a4390f3000c9a5 \
+    --hash=sha256:d374d36726746c81a49f3ff8daa2898dccab6596864ebe43d50733275c629175 \
+    --hash=sha256:de817e2f5fc75a9e7dd350c4b0f54617b280e26d1631811a43e7e968fa71e3e9 \
+    --hash=sha256:e724cebe1fadc2b23c6f7415bad5ee6239e00a69f30ee423f319c6af70e2a5c0 \
+    --hash=sha256:e72591bcfe7512353bd609875ab38050efe3d55e18934e2f18950c108334b4ff \
+    --hash=sha256:e76be12658a6fa376fcd331b1ea4e58f5a06fd0220653450f0d415b8fd0fbe20 \
+    --hash=sha256:eb8d384a24778abf29afb8e41d68fdd9a156cf6e5390c04cc07bbc24b89e98b5 \
+    --hash=sha256:ed350d6978d28b92939bfeb1a0570c523f6170efc3f0a0ef1f1df287cd4f4960 \
+    --hash=sha256:eef44224729e9525d5261cc8d28d6b11cafc90e6bd0be2157bde69a52ec83024 \
+    --hash=sha256:f4db56635b58cd1a200b0a23744ff44206ee6aa428185e2b6c4a65b3197abdcd \
+    --hash=sha256:fdf5197a21dd660cf19dfd2a3ce79574588f8f5e2dbf21bda9ee2d2b46924d84
+packaging==24.1 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002 \
+    --hash=sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124
+pandas==2.2.2 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:001910ad31abc7bf06f49dcc903755d2f7f3a9186c0c040b827e522e9cef0863 \
+    --hash=sha256:0ca6377b8fca51815f382bd0b697a0814c8bda55115678cbc94c30aacbb6eff2 \
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+    --hash=sha256:c7adfc142dac335d8c1e0dcbd37eb8617eac386596eb9e1a1b77791cf2498238 \
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+    --hash=sha256:e9b79011ff7a0f4b1d6da6a61aa1aa604fb312d6647de5bad20013682d1429ce \
+    --hash=sha256:eee3a87076c0756de40b05c5e9a6069c035ba43e8dd71c379e68cab2c20f16ad
+pillow==10.4.0 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:02a2be69f9c9b8c1e97cf2713e789d4e398c751ecfd9967c18d0ce304efbf885 \
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+    --hash=sha256:ff337c552345e95702c5fde3158acb0625111017d0e5f24bf3acdb9cc16b90d1
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+    --hash=sha256:14a9528a8b70ccc7a847637c29e56fd1f9183a9db743bbc5b8e0c4ad60592a82 \
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+    --hash=sha256:f92fe93bc72e262b7b3f2bba9879897e2d58a989b4714ba6a5a7273e842ad2f8
+semantic-version==2.10.0 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:bdabb6d336998cbb378d4b9db3a4b56a1e3235701dc05ea2690d9a997ed5041c \
+    --hash=sha256:de78a3b8e0feda74cabc54aab2da702113e33ac9d9eb9d2389bcf1f58b7d9177
+shellingham==1.5.4 ; python_version >= "3.12" and python_version < "4.0" and sys_platform != "emscripten" \
+    --hash=sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686 \
+    --hash=sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de
+six==1.16.0 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
+    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
+sniffio==1.3.1 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 \
+    --hash=sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc
+starlette==0.38.2 ; python_version >= "3.12" and python_version < "4.0" \
+    --hash=sha256:4ec6a59df6bbafdab5f567754481657f7ed90dc9d69b0c9ff017907dd54faeff \
+    --hash=sha256:c7c0441065252160993a1a37cf2a73bb64d271b17303e0b0c1eb7191cfb12d75
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+    --hash=sha256:01f0477981119c7d8ee0f67ebe0297a7c95b14cf9f4b102b45486deb77018716 \
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+    --hash=sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad
+typer==0.12.5 ; python_version >= "3.12" and python_version < "4.0" and sys_platform != "emscripten" \
+    --hash=sha256:62fe4e471711b147e3365034133904df3e235698399bc4de2b36c8579298d52b \
+    --hash=sha256:f592f089bedcc8ec1b974125d64851029c3b1af145f04aca64d69410f0c9b722
+typing-extensions==4.12.2 ; python_version >= "3.12" and python_version < "4.0" \
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+    --hash=sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8
+tzdata==2024.1 ; python_version >= "3.12" and python_version < "4.0" \
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+    --hash=sha256:65fd46fe3fda5bdc1b03b94eb634923ff18cd35b2f084813ea79d1f103f711b5
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+    --hash=sha256:86d2a77fd490ae3ff6fae1c6ceaecad063d3cc2320b44377efdde79880e11526 \
+    --hash=sha256:88fc51d9a26b10fc331be344f1781224a375b78488fc343620184e95a4b27016 \
+    --hash=sha256:8a34e13a62a59c871064dfd8ffb150867e54291e46d4a7cf11d02c94a5275bae \
+    --hash=sha256:8c82f11964f010053e13daafdc7154ce7385ecc538989a354ccc7067fd7028fd \
+    --hash=sha256:92b2065d642bf8c0a82d59e59053dd2fdde64d4ed44efe4870fa816c1232647b \
+    --hash=sha256:97b52894d948d2f6ea480171a27122d77af14ced35f62e5c892ca2fae9344311 \
+    --hash=sha256:9d9acd80072abcc98bd2c86c3c9cd4ac2347b5a5a0cae7ed5c0ee5675f86d9af \
+    --hash=sha256:9f59a3c656fef341a99e3d63189852be7084c0e54b75734cde571182c087b152 \
+    --hash=sha256:aa5003845cdd21ac0dc6c9bf661c5beddd01116f6eb9eb3c8e272353d45b3288 \
+    --hash=sha256:b16fff62b45eccb9c7abb18e60e7e446998093cdcb50fed33134b9b6878836de \
+    --hash=sha256:b30c6590146e53149f04e85a6e4fcae068df4289e31e4aee1fdf56a0dead8f97 \
+    --hash=sha256:b58cbf0697721120866820b89f93659abc31c1e876bf20d0b3d03cef14faf84d \
+    --hash=sha256:b67c6f5e5a401fc56394f191f00f9b3811fe843ee93f4a70df3c389d1adf857d \
+    --hash=sha256:bceab846bac555aff6427d060f2fcfff71042dba6f5fca7dc4f75cac815e57ca \
+    --hash=sha256:bee9fcb41db2a23bed96c6b6ead6489702c12334ea20a297aa095ce6d31370d0 \
+    --hash=sha256:c114e8da9b475739dde229fd3bc6b05a6537a88a578358bc8eb29b4030fac9c9 \
+    --hash=sha256:c1f0524f203e3bd35149f12157438f406eff2e4fb30f71221c8a5eceb3617b6b \
+    --hash=sha256:c792ea4eabc0159535608fc5658a74d1a81020eb35195dd63214dcf07556f67e \
+    --hash=sha256:c7f3cb904cce8e1be667c7e6fef4516b98d1a6a0635a58a57528d577ac18a128 \
+    --hash=sha256:d67ac60a307f760c6e65dad586f556dde58e683fab03323221a4e530ead6f74d \
+    --hash=sha256:dcacf2c7a6c3a84e720d1bb2b543c675bf6c40e460300b628bab1b1efc7c034c \
+    --hash=sha256:de36fe9c02995c7e6ae6efe2e205816f5f00c22fd1fbf343d4d18c3d5ceac2f5 \
+    --hash=sha256:def07915168ac8f7853812cc593c71185a16216e9e4fa886358a17ed0fd9fcf6 \
+    --hash=sha256:df41b9bc27c2c25b486bae7cf42fccdc52ff181c8c387bfd026624a491c2671b \
+    --hash=sha256:e052b8467dd07d4943936009f46ae5ce7b908ddcac3fda581656b1b19c083d9b \
+    --hash=sha256:e063b1865974611313a3849d43f2c3f5368093691349cf3c7c8f8f75ad7cb280 \
+    --hash=sha256:e1459677e5d12be8bbc7584c35b992eea142911a6236a3278b9b5ce3326f282c \
+    --hash=sha256:e1a99a7a71631f0efe727c10edfba09ea6bee4166a6f9c19aafb6c0b5917d09c \
+    --hash=sha256:e590228200fcfc7e9109509e4d9125eace2042fd52b595dd22bbc34bb282307f \
+    --hash=sha256:e6316827e3e79b7b8e7d8e3b08f4e331af91a48e794d5d8b099928b6f0b85f20 \
+    --hash=sha256:e7837cb169eca3b3ae94cc5787c4fed99eef74c0ab9506756eea335e0d6f3ed8 \
+    --hash=sha256:e848f46a58b9fcf3d06061d17be388caf70ea5b8cc3466251963c8345e13f7eb \
+    --hash=sha256:ed058398f55163a79bb9f06a90ef9ccc063b204bb346c4de78efc5d15abfe602 \
+    --hash=sha256:f2e58f2c36cc52d41f2659e4c0cbf7353e28c8c9e63e30d8c6d3494dc9fdedcf \
+    --hash=sha256:f467ba0050b7de85016b43f5a22b46383ef004c4f672148a8abf32bc999a87f0 \
+    --hash=sha256:f61bdb1df43dc9c131791fbc2355535f9024b9a04398d3bd0684fc16ab07df74 \
+    --hash=sha256:fb06eea71a00a7af0ae6aefbb932fb8a7df3cb390cc217d51a9ad7343de1b8d0 \
+    --hash=sha256:ffd7dcaf744f25f82190856bc26ed81721508fc5cbf2a330751e135ff1283564