Update app.py

app.py

@@ -1,345 +1,198 @@
-import subprocess
-import gradio as gr
+import streamlit as st
 import pandas as pd
-from apscheduler.schedulers.background import BackgroundScheduler
-from huggingface_hub import snapshot_download
-
-from src.about import (
-    CITATION_BUTTON_LABEL,
-    CITATION_BUTTON_TEXT,
-    EVALUATION_QUEUE_TEXT,
-    INTRODUCTION_TEXT,
-    LLM_BENCHMARKS_TEXT,
-    TITLE,
-)
-from src.display.css_html_js import custom_css
-from src.display.utils import (
-    BENCHMARK_COLS,
-    COLS,
-    EVAL_COLS,
-    EVAL_TYPES,
-    NUMERIC_INTERVALS,
-    TYPES,
-    AutoEvalColumn,
-    ModelType,
-    fields,
-    WeightType,
-    Precision
+import folium
+from streamlit_folium import folium_static
+import jdatetime
+import plotly.express as px
+import json
+from pathlib import Path
+
+# تنظیمات اولیه صفحه
+st.set_page_config(
+    page_title="مدیریت چاهک‌های مزارع نیشکر",
+    page_icon="🌱",
+    layout="wide",
+    initial_sidebar_state="expanded"
 )
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
-from src.populate import get_evaluation_queue_df, get_leaderboard_df
-from src.submission.submit import add_new_eval
-
 
-def restart_space():
-    API.restart_space(repo_id=REPO_ID)
+# CSS برای راست به چپ کردن متن‌ها
+st.markdown(
+    """
+    <style>
+    .css-1d391kg {
+        direction: rtl;
+    }
+    .stButton>button {
+        float: right;
+    }
+    .stSelectbox>div>div {
+        direction: rtl;
+        text-align: right;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
 
-try:
-    print(EVAL_REQUESTS_PATH)
-    snapshot_download(
-        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
-    )
-except Exception:
-    restart_space()
-try:
-    print(EVAL_RESULTS_PATH)
-    snapshot_download(
-        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+# تعریف داده‌های اولیه
+@st.cache_data
+def load_initial_data():
+    data = {
+        'مزرعه': ['01-28', '02-19', '02-27', '03-08', '03-30', '03-32'],
+        'کانال': ['01', '02', '02', '03', '03', '03'],
+        'اداره': [1, 2, 2, 1, 1, 1],
+        'واریته': ['CP69', 'CP69', 'CP69', 'CP69', 'IR01-412', 'IRC00-14'],
+        'مساحت': [23.83, 24.53, 24.84, 23.17, 23.35, 22.97],
+        'چاهک_1_نصب': [False] * 6,
+        'چاهک_1_تکنسین': [''] * 6,
+        'چاهک_1_تاریخ': [''] * 6,
+        'چاهک_1_lat': [0.0] * 6,
+        'چاهک_1_lon': [0.0] * 6,
+        'چاهک_2_نصب': [False] * 6,
+        'چاهک_2_تکنسین': [''] * 6,
+        'چاهک_2_تاریخ': [''] * 6,
+        'چاهک_2_lat': [0.0] * 6,
+        'چاهک_2_lon': [0.0] * 6,
+    }
+    return pd.DataFrame(data)
+
+# لود داده‌ها
+if 'farms_data' not in st.session_state:
+    st.session_state.farms_data = load_initial_data()
+
+# تعریف تکنسین‌ها
+TECHNICIANS = [
+    'علی دغاغله',
+    'علیرضا حمیدی',
+    'مسعود بن موسی',
+    'امین هنری'
+]
+
+# عنوان اصلی
+st.title("🌱 مدیریت چاهک‌های مزارع نیشکر")
+
+# ساید‌بار برای فیلتر کردن
+with st.sidebar:
+    st.header("فیلترها")
+    selected_department = st.selectbox(
+        "انتخاب اداره",
+        options=[0, 1, 2, 3, 4],
+        format_func=lambda x: "همه ادارات" if x == 0 else f"اداره {x}",
     )
-except Exception:
-    restart_space()
-
-
-raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-leaderboard_df = original_df.copy()
-
-(
-    finished_eval_queue_df,
-    running_eval_queue_df,
-    pending_eval_queue_df,
-) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
-
-
-# Searching and filtering
-def update_table(
-    hidden_df: pd.DataFrame,
-    columns: list,
-    type_query: list,
-    precision_query: str,
-    size_query: list,
-    show_deleted: bool,
-    query: str,
-):
-    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
-    filtered_df = filter_queries(query, filtered_df)
-    df = select_columns(filtered_df, columns)
-    return df
-
-
-def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
-    return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]
-
-
-def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
-    always_here_cols = [
-        AutoEvalColumn.model_type_symbol.name,
-        AutoEvalColumn.model.name,
-    ]
-    # We use COLS to maintain sorting
-    filtered_df = df[
-        always_here_cols + [c for c in COLS if c in df.columns and c in columns] 
-    ]
-    return filtered_df
-
-
-def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
-    final_df = []
-    if query != "":
-        queries = [q.strip() for q in query.split(";")]
-        for _q in queries:
-            _q = _q.strip()
-            if _q != "":
-                temp_filtered_df = search_table(filtered_df, _q)
-                if len(temp_filtered_df) > 0:
-                    final_df.append(temp_filtered_df)
-        if len(final_df) > 0:
-            filtered_df = pd.concat(final_df)
-            filtered_df = filtered_df.drop_duplicates(
-                subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
-            )
-
-    return filtered_df
-
-
-def filter_models(
-    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
-) -> pd.DataFrame:
-    # Show all models
-    if show_deleted:
-        filtered_df = df
-    else:  # Show only still on the hub models
-        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
-
-    type_emoji = [t[0] for t in type_query]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
-
-    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
-    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
-    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
-    filtered_df = filtered_df.loc[mask]
-
-    return filtered_df
-
-
-demo = gr.Blocks(css=custom_css)
-with demo:
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
-
-    with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        search_bar = gr.Textbox(
-                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
-                            show_label=False,
-                            elem_id="search-bar",
-                        )
-                    with gr.Row():
-                        shown_columns = gr.CheckboxGroup(
-                            choices=[
-                                c.name
-                                for c in fields(AutoEvalColumn)
-                                if not c.hidden and not c.never_hidden
-                            ],
-                            value=[
-                                c.name
-                                for c in fields(AutoEvalColumn)
-                                if c.displayed_by_default and not c.hidden and not c.never_hidden
-                            ],
-                            label="Select columns to show",
-                            elem_id="column-select",
-                            interactive=True,
-                        )
-                    with gr.Row():
-                        deleted_models_visibility = gr.Checkbox(
-                            value=False, label="Show gated/private/deleted models", interactive=True
-                        )
-                with gr.Column(min_width=320):
-                    #with gr.Box(elem_id="box-filter"):
-                    filter_columns_type = gr.CheckboxGroup(
-                        label="Model types",
-                        choices=[t.to_str() for t in ModelType],
-                        value=[t.to_str() for t in ModelType],
-                        interactive=True,
-                        elem_id="filter-columns-type",
-                    )
-                    filter_columns_precision = gr.CheckboxGroup(
-                        label="Precision",
-                        choices=[i.value.name for i in Precision],
-                        value=[i.value.name for i in Precision],
-                        interactive=True,
-                        elem_id="filter-columns-precision",
-                    )
-                    filter_columns_size = gr.CheckboxGroup(
-                        label="Model sizes (in billions of parameters)",
-                        choices=list(NUMERIC_INTERVALS.keys()),
-                        value=list(NUMERIC_INTERVALS.keys()),
-                        interactive=True,
-                        elem_id="filter-columns-size",
-                    )
-
-            leaderboard_table = gr.components.Dataframe(
-                value=leaderboard_df[
-                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
-                    + shown_columns.value
-                ],
-                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
-                datatype=TYPES,
-                elem_id="leaderboard-table",
-                interactive=False,
-                visible=True,
-            )
-
-            # Dummy leaderboard for handling the case when the user uses backspace key
-            hidden_leaderboard_table_for_search = gr.components.Dataframe(
-                value=original_df[COLS],
-                headers=COLS,
-                datatype=TYPES,
-                visible=False,
-            )
-            search_bar.submit(
-                update_table,
-                [
-                    hidden_leaderboard_table_for_search,
-                    shown_columns,
-                    filter_columns_type,
-                    filter_columns_precision,
-                    filter_columns_size,
-                    deleted_models_visibility,
-                    search_bar,
-                ],
-                leaderboard_table,
-            )
-            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
-                selector.change(
-                    update_table,
-                    [
-                        hidden_leaderboard_table_for_search,
-                        shown_columns,
-                        filter_columns_type,
-                        filter_columns_precision,
-                        filter_columns_size,
-                        deleted_models_visibility,
-                        search_bar,
-                    ],
-                    leaderboard_table,
-                    queue=True,
-                )
-
-        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
-            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
-
-        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
-            with gr.Column():
-                with gr.Row():
-                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
-
-                with gr.Column():
-                    with gr.Accordion(
-                        f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            finished_eval_table = gr.components.Dataframe(
-                                value=finished_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-                    with gr.Accordion(
-                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            running_eval_table = gr.components.Dataframe(
-                                value=running_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-
-                    with gr.Accordion(
-                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            pending_eval_table = gr.components.Dataframe(
-                                value=pending_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-            with gr.Row():
-                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
-
-            with gr.Row():
-                with gr.Column():
-                    model_name_textbox = gr.Textbox(label="Model name")
-                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
-                    model_type = gr.Dropdown(
-                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
-                        label="Model type",
-                        multiselect=False,
-                        value=None,
-                        interactive=True,
+    
+    search_term = st.text_input("جستجو بر اساس نام مزرعه یا کانال")
+
+# فیلتر کردن داده‌ها
+filtered_data = st.session_state.farms_data.copy()
+if selected_department != 0:
+    filtered_data = filtered_data[filtered_data['اداره'] == selected_department]
+if search_term:
+    mask = (filtered_data['مزرعه'].str.contains(search_term)) | (filtered_data['کانال'].str.contains(search_term))
+    filtered_data = filtered_data[mask]
+
+# نمایش آمار کلی
+col1, col2, col3 = st.columns(3)
+with col1:
+    total_wells = len(filtered_data) * 2
+    installed_wells = filtered_data['چاهک_1_نصب'].sum() + filtered_data['چاهک_2_نصب'].sum()
+    st.metric("کل چاهک‌ها", total_wells)
+
+with col2:
+    st.metric("چاهک‌های نصب شده", installed_wells)
+
+with col3:
+    st.metric("چاهک‌های باقی‌مانده", total_wells - installed_wells)
+
+# نمایش نقشه
+st.header("🗺️ نقشه چاهک‌ها")
+m = folium.Map(location=[31.3183, 48.6706], zoom_start=10)
+
+for idx, row in filtered_data.iterrows():
+    if row['چاهک_1_نصب'] and row['چاهک_1_lat'] != 0:
+        folium.Marker(
+            [row['چاهک_1_lat'], row['چاهک_1_lon']],
+            popup=f"مزرعه: {row['مزرعه']}<br>چاهک: 1<br>تکنسین: {row['چاهک_1_تکنسین']}<br>تاریخ: {row['چاهک_1_تاریخ']}",
+            icon=folium.Icon(color='red')
+        ).add_to(m)
+    
+    if row['چاهک_2_نصب'] and row['چاهک_2_lat'] != 0:
+        folium.Marker(
+            [row['چاهک_2_lat'], row['چاهک_2_lon']],
+            popup=f"مزرعه: {row['مزرعه']}<br>چاهک: 2<br>تکنسین: {row['چاهک_2_تکنسین']}<br>تاریخ: {row['چاهک_2_تاریخ']}",
+            icon=folium.Icon(color='blue')
+        ).add_to(m)
+
+folium_static(m)
+
+# لیست مزارع و ثبت چاهک
+st.header("📝 لیست مزارع و ثبت چاهک")
+
+for idx, row in filtered_data.iterrows():
+    with st.expander(f"مزرعه {row['مزرعه']} - کانال {row['کانال']}"):
+        st.write(f"واریته: {row['واریته']}")
+        st.write(f"مساحت: {row['مساحت']} هکتار")
+        
+        col1, col2 = st.columns(2)
+        
+        # چاهک 1
+        with col1:
+            st.subheader("چاهک 1")
+            if not row['چاهک_1_نصب']:
+                if st.button(f"ثبت نصب چاهک 1 - مزرعه {row['مزرعه']}", key=f"well1_{idx}"):
+                    technician = st.selectbox(
+                        "انتخاب تکنسین",
+                        options=TECHNICIANS,
+                        key=f"tech1_{idx}"
                     )
-
-                with gr.Column():
-                    precision = gr.Dropdown(
-                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
-                        label="Precision",
-                        multiselect=False,
-                        value="float16",
-                        interactive=True,
-                    )
-                    weight_type = gr.Dropdown(
-                        choices=[i.value.name for i in WeightType],
-                        label="Weights type",
-                        multiselect=False,
-                        value="Original",
-                        interactive=True,
+                    lat = st.number_input("عرض جغرافیایی", value=31.3183, key=f"lat1_{idx}")
+                    lon = st.number_input("طول جغرافیایی", value=48.6706, key=f"lon1_{idx}")
+                    if st.button("ثبت نهایی", key=f"save1_{idx}"):
+                        st.session_state.farms_data.at[idx, 'چاهک_1_نصب'] = True
+                        st.session_state.farms_data.at[idx, 'چاهک_1_تکنسین'] = technician
+                        st.session_state.farms_data.at[idx, 'چاهک_1_تاریخ'] = jdatetime.datetime.now().strftime("%Y/%m/%d")
+                        st.session_state.farms_data.at[idx, 'چاهک_1_lat'] = lat
+                        st.session_state.farms_data.at[idx, 'چاهک_1_lon'] = lon
+                        st.success("چاهک 1 با موفقیت ثبت شد")
+                        st.rerun()
+            else:
+                st.info(f"نصب شده توسط {row['چاهک_1_تکنسین']} در تاریخ {row['چاهک_1_تاریخ']}")
+        
+        # چاهک 2
+        with col2:
+            st.subheader("چاهک 2")
+            if not row['چاهک_2_نصب']:
+                if st.button(f"ثبت نصب چاهک 2 - مزرعه {row['مزرعه']}", key=f"well2_{idx}"):
+                    technician = st.selectbox(
+                        "انتخاب تکنسین",
+                        options=TECHNICIANS,
+                        key=f"tech2_{idx}"
                     )
-                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
-
-            submit_button = gr.Button("Submit Eval")
-            submission_result = gr.Markdown()
-            submit_button.click(
-                add_new_eval,
-                [
-                    model_name_textbox,
-                    base_model_name_textbox,
-                    revision_name_textbox,
-                    precision,
-                    weight_type,
-                    model_type,
-                ],
-                submission_result,
-            )
-
-    with gr.Row():
-        with gr.Accordion("📙 Citation", open=False):
-            citation_button = gr.Textbox(
-                value=CITATION_BUTTON_TEXT,
-                label=CITATION_BUTTON_LABEL,
-                lines=20,
-                elem_id="citation-button",
-                show_copy_button=True,
-            )
-
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=1800)
-scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+                    lat = st.number_input("عرض جغرافیایی", value=31.3183, key=f"lat2_{idx}")
+                    lon = st.number_input("طول جغرافیایی", value=48.6706, key=f"lon2_{idx}")
+                    if st.button("ثبت نهایی", key=f"save2_{idx}"):
+                        st.session_state.farms_data.at[idx, 'چاهک_2_نصب'] = True
+                        st.session_state.farms_data.at[idx, 'چاهک_2_تکنسین'] = technician
+                        st.session_state.farms_data.at[idx, 'چاهک_2_تاریخ'] = jdatetime.datetime.now().strftime("%Y/%m/%d")
+                        st.session_state.farms_data.at[idx, 'چاهک_2_lat'] = lat
+                        st.session_state.farms_data.at[idx, 'چاهک_2_lon'] = lon
+                        st.success("چاهک 2 با موفقیت ثبت شد")
+                        st.rerun()
+            else:
+                st.info(f"نصب شده توسط {row['چاهک_2_تکنسین']} در تاریخ {row['چاهک_2_تاریخ']}")
+
+# نمودار پیشرفت نصب به تفکیک اداره
+st.header("📊 گزارش پیشرفت نصب")
+progress_data = []
+for dept in range(1, 5):
+    dept_data = st.session_state.farms_data[st.session_state.farms_data['اداره'] == dept]
+    total = len(dept_data) * 2
+    installed = dept_data['چاهک_1_نصب'].sum() + dept_data['چاهک_2_نصب'].sum()
+    progress_data.append({
+        'اداره': f'اداره {dept}',
+        'درصد پیشرفت': (installed / total * 100) if total > 0 else 0
+    })
+
+progress_df = pd.DataFrame(progress_data)
+fig = px.bar(progress_df, x='اداره', y='درصد پیشرفت',
+             title='درصد پیشرفت نصب چاهک‌ها به تفکیک اداره')
+st.plotly_chart(fig)