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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Untitled.gif filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.9-slim-buster
+
+# Set the working directory in the container to /app
+WORKDIR /app
+
+# Copy the requirements file
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install -r requirements.txt
+
+# Copy the application code
+COPY . .
+
+# Run the command to start the app
+CMD ["python", "app.py"]

Dockerfile copy

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+FROM python:3.9-slim-buster
+
+# Set the working directory in the container to /app
+WORKDIR /app
+
+# Copy the requirements file
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install -r requirements.txt
+
+# Copy the application code
+COPY . .
+
+
+# chown all the files to the app user and switch to the app user
+RUN chown -R app:app /app
+USER app
+
+EXPOSE 7860
+
+# Run app.py when the container launches
+ENTRYPOINT ["gunicorn", "--bind", "0.0.0.0:7860", "app:app", "--timeout=0", "--workers", "2", "--worker-class", "gthread", "--threads", "3"]

app.py

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+import gradio as gr
+import sqlite3
+import uuid
+from datetime import datetime
+import gradio as gr
+import pandas as pd
+import sqlite3
+from filter import filter_data, sdg_column_mapping
+from tar import sdg_indicators_mapping, sdg_input_mapping, sdg_formulas_mapping
+from deep import filter_and_store_data, data , gradio_app, user_latest_data, create_database ,add_indicator, variable_outputs, get_new_ind, user_late_data, update_indicator_dropdown , sign_in, sign_up, fetch_all_data , calculate_indicator
+from gradio import utils, themes
+from chat2 import demo
+import subprocess
+import time
+
+# Define a custom HTML component with an iframe to display the Dash app
+html_component = gr.HTML("<iframe src='http://localhost:55007' width=100% height='1200'></iframe>")
+
+# Create a Gradio interface with the HTML component
+iplot = gr.Interface(
+    fn=None,  # Set fn to None since we don't need a function
+    inputs=None,
+    outputs=html_component,
+    live=True
+)
+
+iface = gr.Interface(
+    fn=sign_in,
+    inputs=[
+        gr.Textbox(label="Username"),
+        gr.Textbox(label="Password", type="password"),
+    ],
+    outputs="text",
+    title="Sign In",
+    theme='xiaobaiyuan/theme_brief'
+)
+
+iface2 = gr.Interface(
+    fn=sign_up,
+    inputs=[
+        gr.Textbox(label="Username"),
+        gr.Textbox(label="Password", type="password"),
+        gr.Textbox(label="Email", type='email'),
+    ],
+    outputs="text",
+    title="Sign Up",
+    theme='xiaobaiyuan/theme_brief'
+)
+
+create_database()
+max_indicators = 15
+
+with gr.Blocks(visible=True, title="SRL Data Collector", theme='xiaobaiyuan/theme_brief') as ifa:
+    with gr.Row(variant='panel'):
+        username = gr.Textbox(label="Username",visible=False)
+        username2 = gr.Label(label='Username',value=user_late_data())
+        country_dropdown = gr.Dropdown(choices=list(data['Name'].unique()), type="value", label="Country")
+        sdg_dropdown = gr.Dropdown(choices=list(sdg_column_mapping.keys()), type="value", label="SDG Number")
+        initial_sdg = sdg_dropdown.choices[0]
+    with gr.Column(variant='panel'):
+        indicator_dropdown = gr.Dropdown(choices=[update_indicator_dropdown(initial_sdg)], allow_custom_value=True,multiselect=True, interactive=True,type="value", label="Indicator")
+        sdg_dropdown.change(update_indicator_dropdown, sdg_dropdown, indicator_dropdown)
+    with gr.Column(variant='panel'):
+        rows= []
+        for i in range(max_indicators):
+            row = add_indicator(i)
+            row.visible = False
+            rows.append(row)
+    sdg_dropdown.change(variable_outputs, sdg_dropdown, rows)
+    with gr.Row(variant='panel'):
+        save_button = gr.Button("Save") 
+    output = gr.HTML(label="Filtered Data")
+    save_button.click(fn=gradio_app, inputs=[username2, country_dropdown, sdg_dropdown, output], outputs=output)
+app = gr.Blocks(theme='xiaobaiyuan/theme_brief')
+
+
+with gr.Blocks(theme='xiaobaiyuan/theme_brief') as calo:
+    show = gr.Button("Show me my data!")
+    with gr.Row():
+        user = gr.Label(label="Username")
+        countr = gr.Label(label="Country Name")
+        sdg = gr.Label(label="SDG Number")
+    with gr.Row():
+        indi = gr.Label(label="Indicator")
+        name = gr.Label(label="Names")
+        inp1 = gr.Label(label="Input First")
+    with gr.Row():
+        res = gr.HTML(label="Result")
+
+    show.click(fn=fetch_all_data, inputs=None, outputs=[user, countr, sdg, res, indi, name, inp1])
+    calculate = gr.Button("Calculate")
+
+    calculate.click(fn=calculate_indicator, inputs=[user, countr, sdg], outputs=res)
+
+with app:
+    gr.Markdown("""
+    # SRL App
+    """)
+    tab1 = gr.Tab("Sign In")
+    tab2 = gr.Tab("Sign Up")
+    tab3 = gr.Tab("Data Collect")
+    tab4 = gr.Tab("Data Processing")
+    tab5 = gr.Tab("SRL Dash")
+    tab6 = gr.Tab("AI Chat")
+
+    with tab1:
+        iface.render()
+
+    with tab2:
+        iface2.render()
+
+    with tab3:
+        if ifa.visible:
+         ifa.render()
+
+    with tab4:
+        calo.render()
+
+    with tab5:
+        iplot.render()
+
+    with tab6:
+        demo.render()
+
+app.launch(server_name="0.0.0.0", share=True)
+
+

chat2.py

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+from huggingface_hub import InferenceClient
+import gradio as gr
+import os
+cwd = os.getcwd()
+
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+def generate(
+    prompt, history, temperature=0.2, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+):
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+
+    formatted_prompt = format_prompt(prompt, history)
+
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+botm = os.path.join(cwd, 'botm.png')
+us = os.path.join(cwd, 'user.png')
+mychatbot = gr.Chatbot(
+    avatar_images=[us, botm], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
+
+demo = gr.ChatInterface(fn=generate, 
+                        chatbot=mychatbot,
+                        title="Discuss with SRL AI Assistant",
+                        retry_btn=None,
+                        undo_btn=None
+                       )
+
+#demo.queue().launch(show_api=False)

deep.py

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+import gradio as gr
+import pandas as pd
+import os
+import sqlite3
+from filter import filter_data, sdg_column_mapping
+from tar import sdg_indicators_mapping, sdg_input_mapping ,sdg_formulas_mapping, sdg_progress_mapping
+from tqdm import tqdm
+import time
+import uuid
+from datetime import datetime
+# Load your dataset
+cwd = os.getcwd()
+csv_file_path = os.path.join(cwd,'Sustainable_Development_Report_2023_(with_indicators)__-2086263501583264136.csv')
+data = pd.read_csv(csv_file_path)
+# Function to create a SQLite database and table if it doesn't exist
+def sign_in(username, password):
+    try:
+        conn = sqlite3.connect('data_storage.db')
+        c = conn.cursor()
+
+        # Check if the user_sessions table exists
+        c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='user_sessions'")
+        if c.fetchone() is None:
+            # If the table doesn't exist, create it
+            c.execute("""
+            CREATE TABLE user_sessions (
+                user_id TEXT PRIMARY KEY,
+                timestamp TEXT
+            )
+            """)
+
+        c.execute("SELECT * FROM user_info WHERE username=? AND password=?", (username, password))
+        user = c.fetchone()
+        conn.close()
+        if user:
+            user_id = str(uuid.uuid4())
+            timestamp = datetime.now()
+            conn = sqlite3.connect('data_storage.db')
+            c = conn.cursor()
+            c.execute("INSERT INTO user_sessions (user_id, timestamp) VALUES (?, ?)", (user_id, timestamp))
+            conn.commit()
+            conn.close()
+            return "Welcome, " + username
+        else:
+            return "Invalid username or password"
+    except Exception as e:
+        return str(e)
+
+def sign_up(username, password, email):
+    try:
+        conn = sqlite3.connect('data_storage.db')
+        c = conn.cursor()
+
+        # Check if the user_info table exists
+        c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='user_info'")
+        if c.fetchone() is None:
+            # If the table doesn't exist, create it
+            c.execute("""
+            CREATE TABLE user_info (
+                username TEXT PRIMARY KEY,
+                password TEXT,
+                email TEXT
+            )
+            """)
+
+        c.execute("INSERT INTO user_info (username, password, email) VALUES (?, ?, ?)", (username, password, email))
+        conn.commit()
+        conn.close()
+        return "Sign up successful"
+    except Exception as e:
+        return str(e)
+
+
+def save_inputs(indicator, names, inputs,user_id, timestamp):
+    # Connect to the SQLite database
+    conn = sqlite3.connect('data_storage.db')
+    c = conn.cursor()
+    timestamp = time.time()
+    # Create table if it doesn't exist
+    c.execute('''CREATE TABLE IF NOT EXISTS inputs
+                 (indicator text, names text, inputs text, user_id, timestamp)''')
+
+    # Insert a row of data
+    c.execute("INSERT INTO inputs VALUES (?, ?, ?, ?,?)", (indicator, str(names), str(inputs), user_id, timestamp))
+
+    # Save (commit) the changes
+    conn.commit()
+
+    # Close the connection
+    conn.close()
+
+def create_database():
+    conn = sqlite3.connect("data_storage.db")
+    cursor = conn.cursor()
+    cursor.execute("""
+    CREATE TABLE IF NOT EXISTS user_data (
+        user_id TEXT PRIMARY KEY,
+        username TEXT,
+        country_name TEXT,
+        sdg_number INTEGER,
+        result TEXT,
+        indicator TEXT,
+        names TEXT,
+        input_first REAL,
+        input_2nd REAL,
+        input_3rd REAL,
+        input_4th REAL,
+        timestamp REAL
+    )
+    """)
+    conn.commit()
+    conn.close()
+
+
+def filter_and_store_data(username, country_name, sdg_number, result, indicator, names, input_first, input_2nd, input_3rd, input_4th, user_id, timestamp):
+    try:
+        # Convert the selected SDG number to an integer
+        sdg_number = int(sdg_number)
+        
+        # Get the list of columns based on the selected SDG number
+        selected_columns = ['Name'] + sdg_column_mapping.get(sdg_number, [])
+        
+        # Filter data based on country and selected columns
+        selected_data = data[(data['Name'] == country_name)][selected_columns]
+        
+        # Convert the selected data to an HTML table with more rows
+        html_table = selected_data.to_html(index=False)
+        
+        # Wrap the HTML table in a div with a fixed height and scrollable overflow
+        scrollable_html_table = f'<div style="height: 300px; overflow-y: scroll;">{html_table}</div>'
+        
+        # Get the current timestamp
+        timestamp = time.time()
+        timestamp = datetime.now()
+        # Store the data in the SQLite database
+        # Store the data in the SQLite database
+        conn = sqlite3.connect("data_storage.db")
+        cursor = conn.cursor()
+        cursor.execute("INSERT INTO user_data (username, country_name, sdg_number, result, indicator, names, input_first, input_2nd, input_3rd, input_4th, user_id, timestamp) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
+                 (username, country_name, sdg_number, str(scrollable_html_table), str(indicator), str(names), input_first, input_2nd, input_3rd, input_4th, user_id, timestamp))
+        conn.commit()
+        conn.close()
+        
+        return scrollable_html_table
+    except ValueError:
+        return "Please select a valid SDG number.", []
+    except Exception as e:
+        return f"An error occurred: {e}", []
+
+
+
+def gradio_app(username, country_name, sdg_number, result, indicator, names, input_first, input_2nd, input_3rd, input_4th, user_id, timestamp):
+    # Call the filter_and_store_data function
+    return filter_and_store_data(username, country_name, sdg_number, result, indicator, names, input_first, input_2nd, input_3rd, input_4th, user_id, timestamp)
+
+def gradios(username, country_name, sdg_number, indicator, names, input_first, input_2nd, input_3rd, input_4th):
+    # Get the formula for the selected indicator
+    formula = sdg_formulas_mapping[indicator]
+
+    # Calculate the result using the formula
+    result = formula(input_first, input_2nd, input_3rd, input_4th)
+
+    # Store the result in the database
+    conn = sqlite3.connect("data_storage.db")
+    cursor = conn.cursor()
+    cursor.execute("""
+    INSERT INTO user_data (username, country_name, sdg_number, result, indicator, names, input_first, input_2nd, input_3rd, input_4th, timestamp)
+    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+    """, (username, country_name, sdg_number, result, indicator, names, input_first, input_2nd, input_3rd, input_4th))
+    conn.commit()
+    conn.close()
+
+    return f"Result: {result}"
+
+def user_latest_data():
+    conn = sqlite3.connect("data_storage.db")
+    df = pd.read_sql_query("SELECT * FROM user_data ORDER BY username DESC LIMIT 1", conn)
+    conn.close()
+
+    # Convert numeric values to strings
+    return str(df['username'].values[0])
+
+
+def user_late_data():
+    conn = sqlite3.connect("data_storage.db")
+    df = pd.read_sql_query("SELECT * FROM user_data ORDER BY username DESC LIMIT 1", conn)
+    conn.close()
+
+    # Check if the DataFrame is not empty and contains the 'username' column
+    if not df.empty and 'username' in df.columns and not df['username'].empty:
+        return str(df['username'].values[0])
+    else:
+        return "No username available"
+    
+def get_nam(indicator):
+    if indicator in sdg_input_mapping:
+        names = [item['name'] for item in sdg_input_mapping[indicator]]
+        return names
+
+def update_nam_dropdown(indicator):
+    return get_nam(indicator)
+
+def add_indicator(sdg_number):
+    max_inputs = 5
+    def variable_output2(k):
+        k = int(k)
+        return [gr.Number(visible=True)]*k + [gr.Number(visible=False)]*(max_inputs-k)
+    with gr.Row(variant='panel') as row:
+        indicator_dropdown2 = gr.Dropdown(choices=list(sdg_input_mapping.keys()), interactive=True, type="value", label="Please choose an Indicator")
+        names_dropdown = gr.Dropdown(choices=[update_nam_dropdown(indicator_dropdown2.choices[0])], allow_custom_value=True,multiselect=True, interactive=True, type="value", label="The indicators Inputs needed respectively")
+        indicator_dropdown2.change(fn=update_nam_dropdown, inputs=indicator_dropdown2, outputs=names_dropdown)
+        with gr.Column(variant='panel'):
+            u = gr.Slider(1, max_inputs, value=max_inputs, step=1, label="How many inputs to fill:")
+            textboxes = []
+            for i in range(max_inputs):
+                t = gr.Number(label=f"Please fill with the input number {i}", interactive=True)
+                textboxes.append(t)
+            
+            u.change(variable_output2, u, textboxes)
+
+            save_button = gr.Button("Save")
+            
+        #save_button.click(save_inputs, inputs=[indicator_dropdown2, names_dropdown, t], outputs=[])
+        def save_inputs_wrapper(indicator, names, *textboxes):
+            return save_inputs(indicator, names, [str(t) if isinstance(t, float) else str(t.value) for t in textboxes])
+        save_button.click(save_inputs_wrapper, inputs=[indicator_dropdown2, names_dropdown, *textboxes], outputs=[])
+    return row
+
+max_indicators = 15
+
+def variable_outputs(k):
+    k = int(k)
+    num_rows = sdg_progress_mapping.get(k, 0) 
+    return [gr.Textbox(visible=True)]*num_rows + [gr.Textbox(visible=False)]*(max_indicators-num_rows)
+
+def get_new_ind(sdg_number):
+    if sdg_number in sdg_indicators_mapping:
+        ind = sdg_indicators_mapping.get(sdg_number, [])
+        return list(ind)
+
+def update_indicator_dropdown(sdg_number):
+    return get_new_ind(sdg_number)
+
+def fetch_all_data():
+    conn = sqlite3.connect("data_storage.db")
+
+    # Fetch user data
+    user_df = pd.read_sql_query("SELECT * FROM user_info", conn)
+    latest_user_row = user_df.iloc[[-1]]
+    user_data = str(latest_user_row['username'].values[0])
+
+    # Fetch user data
+    user_data_df = pd.read_sql_query("SELECT * FROM user_data", conn)
+    latest_user_data_row = user_data_df.iloc[[-1]]
+    user_data_values = (
+        str(latest_user_data_row['country_name'].values[0]),
+        str(latest_user_data_row['sdg_number'].values[0]),
+        str(latest_user_data_row['result'].values[0])
+    )
+
+    # Fetch input data
+    input_df = pd.read_sql_query("SELECT * FROM inputs", conn)
+    latest_input_row = input_df.iloc[[-1]]
+    input_data = (
+        str(latest_input_row['indicator'].values[0]),
+        str(latest_input_row['names'].values[0]),
+        str(latest_input_row['inputs'].values[0])
+    )
+
+    conn.close()
+
+    return [user_data] + list(user_data_values) + list(input_data)
+
+
+
+def calculate_indicator(user_id, timestamp):
+    try:
+        conn = sqlite3.connect("data_storage.db")
+        
+        # Fetch user data based on user_id and timestamp
+        user_data_df = pd.read_sql_query("SELECT * FROM user_data WHERE user_id=? AND timestamp=?", conn, params=(user_id, timestamp))
+        
+        if user_data_df.empty:
+            return "No data found for the specified user_id and timestamp"
+        
+        # Extract relevant information
+        indicator = user_data_df['indicator'].values[0]
+        names = user_data_df['names'].values[0]
+        input_first = user_data_df['input_first'].values[0]
+        
+        # Perform the calculation
+        if indicator in sdg_formulas_mapping:
+            formula = sdg_formulas_mapping[indicator]
+            result = formula(input_first)
+            return f"Result for {indicator}: {result}"
+        else:
+            return f"Indicator {indicator} not found in the dictionary"
+        
+    except Exception as e:
+        return f"An error occurred: {e}"

docker-compose.yml

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+version: '3'
+
+services:
+  app:
+    build: .
+    ports:
+      - "55006:55006"

filter.py

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+
+import pandas as pd
+
+sdg_column_mapping = {
+        0:[
+            'Goal_Rating', 'Goal_Trend', 'Value_sdg_earlyedu', 'Year_sdg_earlyedu', 'impute_sdg_earlyedu',
+            'Score_sdg_earlyedu', 'Rating_sdg_earlyedu', 'Trend_sdg_earlyedu', 'Value_sdg_primary',
+            'Year_sdg_primary', 'Score_sdg_primary', 'Rating_sdg_primary', 'Trend_sdg_primary',
+            'Value_sdg_second', 'Year_sdg_second', 'impute_sdg_second', 'Score_sdg_second', 'Rating_sdg_second',
+            'Trend_sdg_second', 'Value_sdg_literacy', 'Year_sdg_literacy', 'Score_sdg_literacy', 'Rating_sdg_literacy',
+            'Trend_sdg_literacy', 'Value_sdg_tertiary', 'Year_sdg_tertiary', 'Score_sdg_tertiary', 'Rating_sdg_tertiary',
+            'Trend_sdg_tertiary', 'Value_sdg_pisa', 'Year_sdg_pisa', 'Rating_sdg_pisa', 'Trend_sdg_pisa',
+            'Value_sdg_socioec', 'Year_sdg_socioec', 'Rating_sdg_socioec', 'Trend_sdg_socioec', 'Value_sdg_science',
+            'Year_sdg_science', 'Rating_sdg_science', 'Trend_sdg_science',],
+        3:[
+            'Goal_3_Rating', 'Goal_3_Trend', 'Value_sdg3_matmort', 'Year_sdg3_matmort',
+            'Score_sdg3_matmort', 'Rating_sdg3_matmort', 'Trend_sdg3_matmort', 'Value_sdg3_neonat',
+            'Year_sdg3_neonat', 'Score_sdg3_neonat', 'Rating_sdg3_neonat', 'Trend_sdg3_neonat', 'Value_sdg3_u5mort',
+            'Year_sdg3_u5mort', 'Score_sdg3_u5mort', 'Rating_sdg3_u5mort', 'Trend_sdg3_u5mort', 'Value_sdg3_tb',
+            'Year_sdg3_tb', 'Score_sdg3_tb', 'Rating_sdg3_tb', 'Trend_sdg3_tb', 'Value_sdg3_hiv', 'Year_sdg3_hiv',
+            'Score_sdg3_hiv', 'Rating_sdg3_hiv', 'Trend_sdg3_hiv', 'Value_sdg3_ncds', 'Year_sdg3_ncds', 'Score_sdg3_ncds',
+            'Rating_sdg3_ncds', 'Trend_sdg3_ncds', 'Value_sdg3_pollmort', 'Year_sdg3_pollmort', 'Score_sdg3_pollmort',
+            'Rating_sdg3_pollmort', 'Value_sdg3_traffic', 'Year_sdg3_traffic', 'Score_sdg3_traffic', 'Rating_sdg3_traffic',
+            'Trend_sdg3_traffic', 'Value_sdg3_lifee', 'Year_sdg3_lifee', 'Score_sdg3_lifee', 'Rating_sdg3_lifee',
+            'Trend_sdg3_lifee', 'Value_sdg3_fertility', 'Year_sdg3_fertility', 'Score_sdg3_fertility', 'Rating_sdg3_fertility',
+            'Trend_sdg3_fertility', 'Value_sdg3_births', 'Year_sdg3_births', 'Score_sdg3_births', 'Rating_sdg3_births',
+            'Trend_sdg3_births', 'Value_sdg3_vac', 'Year_sdg3_vac', 'Score_sdg3_vac', 'Rating_sdg3_vac', 'Trend_sdg3_vac',
+            'Value_sdg3_uhc', 'Year_sdg3_uhc', 'Score_sdg3_uhc', 'Rating_sdg3_uhc', 'Trend_sdg3_uhc', 'Value_sdg3_swb',
+            'Year_sdg3_swb', 'Score_sdg3_swb', 'Rating_sdg3_swb', 'Trend_sdg3_swb', 'Value_sdg3_region', 'Year_sdg3_region',
+            'Rating_sdg3_region', 'Trend_sdg3_region', 'Value_sdg3_incomeg', 'Year_sdg3_incomeg', 'Rating_sdg3_incomeg',
+            'Trend_sdg3_incomeg', 'Value_sdg3_smoke', 'Year_sdg3_smoke', 'Rating_sdg3_smoke', 'Trend_sdg3_smoke',
+        ],
+        4: [
+            'Goal_4_Rating', 'Goal_4_Trend', 'Value_sdg4_earlyedu', 'Year_sdg4_earlyedu',
+            'Score_sdg4_earlyedu', 'Rating_sdg4_earlyedu', 'Value_sdg4_primary',
+            'Year_sdg4_primary', 'Score_sdg4_primary', 'Rating_sdg4_primary',
+            'Trend_sdg4_primary', 'Value_sdg4_second', 'Year_sdg4_second',
+            'impute_sdg4_second', 'Score_sdg4_second', 'Rating_sdg4_second',
+            'Trend_sdg4_second', 'Value_sdg4_literacy', 'Year_sdg4_literacy',
+            'Score_sdg4_literacy', 'Rating_sdg4_literacy', 'Trend_sdg4_literacy',
+            'Value_sdg4_tertiary', 'Year_sdg4_tertiary', 'Rating_sdg4_tertiary',
+            'Trend_sdg4_tertiary', 'Value_sdg4_pisa', 'Year_sdg4_pisa',
+            'Rating_sdg4_pisa', 'Trend_sdg4_pisa', 'Value_sdg4_socioec',
+            'Year_sdg4_socioec', 'Rating_sdg4_socioec', 'Trend_sdg4_socioec',
+            'Value_sdg4_science', 'Year_sdg4_science', 'Rating_sdg4_science',
+        ],
+        5:[
+            'Goal_5_Rating', 'Goal_5_Trend', 'Value_sdg5_rratio', 'Year_sdg5_rratio',
+            'Score_sdg5_rratio', 'Rating_sdg5_rratio', 'Trend_sdg5_rratio', 'Value_sdg5_vawage',
+            'Year_sdg5_vawage', 'Score_sdg5_vawage', 'Rating_sdg5_vawage', 'Trend_sdg5_vawage', 'Value_sdg5_lfpart',
+            'Year_sdg5_lfpart', 'Score_sdg5_lfpart', 'Rating_sdg5_lfpart', 'Trend_sdg5_lfpart', 'Value_sdg5_parl',
+            'Year_sdg5_parl', 'Score_sdg5_parl', 'Rating_sdg5_parl', 'Trend_sdg5_parl', 'Value_sdg5_fgm', 'Year_sdg5_fgm',
+            'Score_sdg5_fgm', 'Rating_sdg5_fgm', 'Trend_sdg5_fgm', 'Value_sdg5_cmarry', 'Year_sdg5_cmarry', 'Score_sdg5_cmarry',
+            'Rating_sdg5_cmarry', 'Trend_sdg5_cmarry', 'Value_sdg5_vabirth', 'Year_sdg5_vabirth', 'Score_sdg5_vabirth',
+            'Rating_sdg5_vabirth', 'Trend_sdg5_vabirth',
+        ],
+        6:[
+            'Goal_6_Rating', 'Goal_6_Trend', 'Value_sdg6_safewater', 'Year_sdg6_safewater',
+            'Score_sdg6_safewater', 'Rating_sdg6_safewater', 'Trend_sdg6_safewater', 'Value_sdg6_sanitation',
+            'Year_sdg6_sanitation', 'Score_sdg6_sanitation', 'Rating_sdg6_sanitation', 'Trend_sdg6_sanitation',
+            'Value_sdg6_freshwater', 'Year_sdg6_freshwater', 'Score_sdg6_freshwater', 'Rating_sdg6_freshwater',
+            'Trend_sdg6_freshwater', 'Value_sdg6_ecosystems', 'Year_sdg6_ecosystems', 'Score_sdg6_ecosystems',
+            'Rating_sdg6_ecosystems', 'Trend_sdg6_ecosystems', 'Value_sdg6_wateruse', 'Year_sdg6_wateruse',
+            'Score_sdg6_wateruse', 'Rating_sdg6_wateruse', 'Trend_sdg6_wateruse', 'Value_sdg6_waterstress',
+            'Year_sdg6_waterstress', 'Score_sdg6_waterstress', 'Rating_sdg6_waterstress', 'Trend_sdg6_waterstress',
+            'Value_sdg6_quality', 'Year_sdg6_quality', 'Score_sdg6_quality', 'Rating_sdg6_quality', 'Trend_sdg6_quality',
+            'Value_sdg6_wastewater', 'Year_sdg6_wastewater', 'Score_sdg6_wastewater', 'Rating_sdg6_wastewater',
+            'Trend_sdg6_wastewater', 'Value_sdg6_river', 'Year_sdg6_river', 'Score_sdg6_river', 'Rating_sdg6_river',
+            'Trend_sdg6_river', 'Value_sdg6_fisheries', 'Year_sdg6_fisheries', 'Score_sdg6_fisheries',
+            'Rating_sdg6_fisheries', 'Trend_sdg6_fisheries',
+        ],
+        7:[
+            'Goal_7_Rating', 'Goal_7_Trend', 'Value_sdg7_access', 'Year_sdg7_access',
+            'Score_sdg7_access', 'Rating_sdg7_access', 'Trend_sdg7_access', 'Value_sdg7_electrify',
+            'Year_sdg7_electrify', 'Score_sdg7_electrify', 'Rating_sdg7_electrify', 'Trend_sdg7_electrify',
+            'Value_sdg7_renewcon', 'Year_sdg7_renewcon', 'Score_sdg7_renewcon', 'Rating_sdg7_renewcon',
+            'Trend_sdg7_renewcon', 'Value_sdg7_afford', 'Year_sdg7_afford', 'Score_sdg7_afford',
+            'Rating_sdg7_afford', 'Trend_sdg7_afford', 'Value_sdg7_cleancook', 'Year_sdg7_cleancook',
+            'Score_sdg7_cleancook', 'Rating_sdg7_cleancook', 'Trend_sdg7_cleancook', 'Value_sdg7_nonrenewcon',
+            'Year_sdg7_nonrenewcon', 'Score_sdg7_nonrenewcon', 'Rating_sdg7_nonrenewcon',
+            'Trend_sdg7_nonrenewcon', 'Value_sdg7_biofuel', 'Year_sdg7_biofuel', 'Score_sdg7_biofuel',
+            'Rating_sdg7_biofuel', 'Trend_sdg7_biofuel',
+        ],
+        8:[
+            'Goal_8_Rating', 'Goal_8_Trend', 'Value_sdg8_adjgrowth', 'Year_sdg8_adjgrowth',
+            'Score_sdg8_adjgrowth', 'Rating_sdg8_adjgrowth', 'Value_sdg8_slavery', 'Year_sdg8_slavery',
+            'impute_sdg8_slavery', 'Score_sdg8_slavery', 'Rating_sdg8_slavery', 'Value_sdg8_accounts',
+            'Year_sdg8_accounts', 'Score_sdg8_accounts', 'Rating_sdg8_accounts', 'Trend_sdg8_accounts',
+            'Value_sdg8_unemp', 'Year_sdg8_unemp', 'Score_sdg8_unemp', 'Rating_sdg8_unemp',
+            'Trend_sdg8_unemp',
+        ],
+        9:[
+            'Goal_9_Rating', 'Goal_9_Trend', 'Value_sdg9_roads', 'Year_sdg9_roads',
+            'Score_sdg9_roads', 'Rating_sdg9_roads', 'Value_sdg9_intuse', 'Year_sdg9_intuse',
+            'Score_sdg9_intuse', 'Rating_sdg9_intuse', 'Trend_sdg9_intuse', 'Value_sdg9_mobuse',
+            'Year_sdg9_mobuse', 'Score_sdg9_mobuse', 'Rating_sdg9_mobuse', 'Trend_sdg9_mobuse',
+            'Value_sdg9_lpi', 'Year_sdg9_lpi', 'Score_sdg9_lpi', 'Rating_sdg9_lpi', 'Trend_sdg9_lpi',
+            'Value_sdg9_uni', 'Year_sdg9_uni', 'impute_sdg9_uni', 'Score_sdg9_uni', 'Rating_sdg9_uni',
+            'Value_sdg9_articles', 'Year_sdg9_articles', 'Score_sdg9_articles', 'Rating_sdg9_articles',
+            'Trend_sdg9_articles', 'Value_sdg9_rdex', 'Year_sdg9_rdex', 'impute_sdg9_rdex', 'Score_sdg9_rdex',
+            'Rating_sdg9_rdex', 'Trend_sdg9_rdex', 'Value_sdg9_rdres', 'Year_sdg9_rdres',
+            'Rating_sdg9_rdres', 'Trend_sdg9_rdres', 'Value_sdg9_patents', 'Year_sdg9_patents',
+            'Rating_sdg9_patents', 'Trend_sdg9_patents', 'Value_sdg9_netacc', 'Year_sdg9_netacc',
+            'Rating_sdg9_netacc', 'Trend_sdg9_netacc', 'Value_sdg9_womensci', 'Year_sdg9_womensci',
+            'Rating_sdg9_womensci', 'Trend_sdg9_womensci',
+        ],
+        10:[
+            'Goal_10_Rating', 'Goal_10_Trend', 'Value_sdg10_gini', 'Year_sdg10_gini',
+            'Score_sdg10_gini', 'Rating_sdg10_gini', 'Trend_sdg10_gini', 'Value_sdg10_palma',
+            'Year_sdg10_palma', 'Score_sdg10_palma', 'Rating_sdg10_palma', 'Trend_sdg10_palma',
+            'Value_sdg10_elder', 'Year_sdg10_elder', 'Rating_sdg10_elder', 'Trend_sdg10_elder',
+        ],
+        11:[
+            'Goal_11_Rating', 'Goal_11_Trend', 'Value_sdg11_slums', 'Year_sdg11_slums',
+            'impute_sdg11_slums', 'Score_sdg11_slums', 'Rating_sdg11_slums', 'Trend_sdg11_slums',
+            'Value_sdg11_pm25', 'Year_sdg11_pm25', 'Score_sdg11_pm25', 'Rating_sdg11_pm25',
+            'Trend_sdg11_pm25', 'Value_sdg11_pipedwat', 'Year_sdg11_pipedwat',
+            'Score_sdg11_pipedwat', 'Rating_sdg11_pipedwat', 'Trend_sdg11_pipedwat',
+            'Value_sdg11_transport', 'Year_sdg11_transport', 'Score_sdg11_transport',
+            'Rating_sdg11_transport', 'Trend_sdg11_transport', 'Value_sdg11_rentover',
+            'Year_sdg11_rentover', 'Rating_sdg11_rentover', 'Trend_sdg11_rentover',
+            'Value_sdg11_walkable', 'Year_sdg11_walkable', 'Rating_sdg11_walkable',
+        ],
+        12:[
+            'Goal_12_Rating', 'Goal_12_Trend', 'Value_sdg12_waste', 'Year_sdg12_waste',
+            'impute_sdg12_waste', 'Score_sdg12_waste', 'Rating_sdg12_waste', 'Trend_sdg12_waste',
+            'Value_sdg12_recycling', 'Year_sdg12_recycling', 'impute_sdg12_recycling',
+            'Score_sdg12_recycling', 'Rating_sdg12_recycling', 'Trend_sdg12_recycling',
+            'Value_sdg12_solidwaste', 'Year_sdg12_solidwaste', 'impute_sdg12_solidwaste',
+            'Score_sdg12_solidwaste', 'Rating_sdg12_solidwaste', 'Trend_sdg12_solidwaste',
+        ],
+        13:[
+            'Goal_13_Rating', 'Goal_13_Trend', 'Value_sdg13_emissions', 'Year_sdg13_emissions',
+            'impute_sdg13_emissions', 'Score_sdg13_emissions', 'Rating_sdg13_emissions',
+            'Trend_sdg13_emissions', 'Value_sdg13_forests', 'Year_sdg13_forests',
+            'impute_sdg13_forests', 'Score_sdg13_forests', 'Rating_sdg13_forests',
+            'Trend_sdg13_forests',
+        ],
+        14:[
+            'Goal_14_Rating', 'Goal_14_Trend', 'Value_sdg14_overfishing', 'Year_sdg14_overfishing',
+            'impute_sdg14_overfishing', 'Score_sdg14_overfishing', 'Rating_sdg14_overfishing',
+            'Trend_sdg14_overfishing', 'Value_sdg14_mpa', 'Year_sdg14_mpa', 'impute_sdg14_mpa',
+            'Score_sdg14_mpa', 'Rating_sdg14_mpa', 'Trend_sdg14_mpa',
+        ],
+        17:[
+            'Goal_17_Rating', 'Goal_17_Trend', 'Value_sdg17_govex', 'Year_sdg17_govex',
+            'Score_sdg17_govex', 'Rating_sdg17_govex', 'Trend_sdg17_govex', 'Value_sdg17_oda',
+            'Year_sdg17_oda', 'Score_sdg17_oda', 'Rating_sdg17_oda', 'Trend_sdg17_oda',
+            'Value_sdg17_fp', 'Year_sdg17_fp', 'impute_sdg17_fp', 'Score_sdg17_fp',
+            'Rating_sdg17_fp', 'Trend_sdg17_fp',
+        ]
+    }
+
+def filter_data(country_name, sdg_number):
+    # Load your dataset
+    data = pd.read_csv(r'C:\Users\Samer\OneDrive\DS\SDG\new\Sustainable_Development_Report_2023_(with_indicators)__-2086263501583264136.csv')
+    
+    # Create a mapping of SDG numbers to their corresponding columns
+    sdg_column_mapping = {
+        0:[
+            'Goal_Rating', 'Goal_Trend', 'Value_sdg_earlyedu', 'Year_sdg_earlyedu', 'impute_sdg_earlyedu',
+            'Score_sdg_earlyedu', 'Rating_sdg_earlyedu', 'Trend_sdg_earlyedu', 'Value_sdg_primary',
+            'Year_sdg_primary', 'Score_sdg_primary', 'Rating_sdg_primary', 'Trend_sdg_primary',
+            'Value_sdg_second', 'Year_sdg_second', 'impute_sdg_second', 'Score_sdg_second', 'Rating_sdg_second',
+            'Trend_sdg_second', 'Value_sdg_literacy', 'Year_sdg_literacy', 'Score_sdg_literacy', 'Rating_sdg_literacy',
+            'Trend_sdg_literacy', 'Value_sdg_tertiary', 'Year_sdg_tertiary', 'Score_sdg_tertiary', 'Rating_sdg_tertiary',
+            'Trend_sdg_tertiary', 'Value_sdg_pisa', 'Year_sdg_pisa', 'Rating_sdg_pisa', 'Trend_sdg_pisa',
+            'Value_sdg_socioec', 'Year_sdg_socioec', 'Rating_sdg_socioec', 'Trend_sdg_socioec', 'Value_sdg_science',
+            'Year_sdg_science', 'Rating_sdg_science', 'Trend_sdg_science',],
+        3:[
+            'Goal_3_Rating', 'Goal_3_Trend', 'Value_sdg3_matmort', 'Year_sdg3_matmort',
+            'Score_sdg3_matmort', 'Rating_sdg3_matmort', 'Trend_sdg3_matmort', 'Value_sdg3_neonat',
+            'Year_sdg3_neonat', 'Score_sdg3_neonat', 'Rating_sdg3_neonat', 'Trend_sdg3_neonat', 'Value_sdg3_u5mort',
+            'Year_sdg3_u5mort', 'Score_sdg3_u5mort', 'Rating_sdg3_u5mort', 'Trend_sdg3_u5mort', 'Value_sdg3_tb',
+            'Year_sdg3_tb', 'Score_sdg3_tb', 'Rating_sdg3_tb', 'Trend_sdg3_tb', 'Value_sdg3_hiv', 'Year_sdg3_hiv',
+            'Score_sdg3_hiv', 'Rating_sdg3_hiv', 'Trend_sdg3_hiv', 'Value_sdg3_ncds', 'Year_sdg3_ncds', 'Score_sdg3_ncds',
+            'Rating_sdg3_ncds', 'Trend_sdg3_ncds', 'Value_sdg3_pollmort', 'Year_sdg3_pollmort', 'Score_sdg3_pollmort',
+            'Rating_sdg3_pollmort', 'Value_sdg3_traffic', 'Year_sdg3_traffic', 'Score_sdg3_traffic', 'Rating_sdg3_traffic',
+            'Trend_sdg3_traffic', 'Value_sdg3_lifee', 'Year_sdg3_lifee', 'Score_sdg3_lifee', 'Rating_sdg3_lifee',
+            'Trend_sdg3_lifee', 'Value_sdg3_fertility', 'Year_sdg3_fertility', 'Score_sdg3_fertility', 'Rating_sdg3_fertility',
+            'Trend_sdg3_fertility', 'Value_sdg3_births', 'Year_sdg3_births', 'Score_sdg3_births', 'Rating_sdg3_births',
+            'Trend_sdg3_births', 'Value_sdg3_vac', 'Year_sdg3_vac', 'Score_sdg3_vac', 'Rating_sdg3_vac', 'Trend_sdg3_vac',
+            'Value_sdg3_uhc', 'Year_sdg3_uhc', 'Score_sdg3_uhc', 'Rating_sdg3_uhc', 'Trend_sdg3_uhc', 'Value_sdg3_swb',
+            'Year_sdg3_swb', 'Score_sdg3_swb', 'Rating_sdg3_swb', 'Trend_sdg3_swb', 'Value_sdg3_region', 'Year_sdg3_region',
+            'Rating_sdg3_region', 'Trend_sdg3_region', 'Value_sdg3_incomeg', 'Year_sdg3_incomeg', 'Rating_sdg3_incomeg',
+            'Trend_sdg3_incomeg', 'Value_sdg3_smoke', 'Year_sdg3_smoke', 'Rating_sdg3_smoke', 'Trend_sdg3_smoke',
+        ],
+        4: [
+            'Goal_4_Rating', 'Goal_4_Trend', 'Value_sdg4_earlyedu', 'Year_sdg4_earlyedu',
+            'Score_sdg4_earlyedu', 'Rating_sdg4_earlyedu', 'Value_sdg4_primary',
+            'Year_sdg4_primary', 'Score_sdg4_primary', 'Rating_sdg4_primary',
+            'Trend_sdg4_primary', 'Value_sdg4_second', 'Year_sdg4_second',
+            'impute_sdg4_second', 'Score_sdg4_second', 'Rating_sdg4_second',
+            'Trend_sdg4_second', 'Value_sdg4_literacy', 'Year_sdg4_literacy',
+            'Score_sdg4_literacy', 'Rating_sdg4_literacy', 'Trend_sdg4_literacy',
+            'Value_sdg4_tertiary', 'Year_sdg4_tertiary', 'Rating_sdg4_tertiary',
+            'Trend_sdg4_tertiary', 'Value_sdg4_pisa', 'Year_sdg4_pisa',
+            'Rating_sdg4_pisa', 'Trend_sdg4_pisa', 'Value_sdg4_socioec',
+            'Year_sdg4_socioec', 'Rating_sdg4_socioec', 'Trend_sdg4_socioec',
+            'Value_sdg4_science', 'Year_sdg4_science', 'Rating_sdg4_science',
+        ],
+        5:[
+            'Goal_5_Rating', 'Goal_5_Trend', 'Value_sdg5_rratio', 'Year_sdg5_rratio',
+            'Score_sdg5_rratio', 'Rating_sdg5_rratio', 'Trend_sdg5_rratio', 'Value_sdg5_vawage',
+            'Year_sdg5_vawage', 'Score_sdg5_vawage', 'Rating_sdg5_vawage', 'Trend_sdg5_vawage', 'Value_sdg5_lfpart',
+            'Year_sdg5_lfpart', 'Score_sdg5_lfpart', 'Rating_sdg5_lfpart', 'Trend_sdg5_lfpart', 'Value_sdg5_parl',
+            'Year_sdg5_parl', 'Score_sdg5_parl', 'Rating_sdg5_parl', 'Trend_sdg5_parl', 'Value_sdg5_fgm', 'Year_sdg5_fgm',
+            'Score_sdg5_fgm', 'Rating_sdg5_fgm', 'Trend_sdg5_fgm', 'Value_sdg5_cmarry', 'Year_sdg5_cmarry', 'Score_sdg5_cmarry',
+            'Rating_sdg5_cmarry', 'Trend_sdg5_cmarry', 'Value_sdg5_vabirth', 'Year_sdg5_vabirth', 'Score_sdg5_vabirth',
+            'Rating_sdg5_vabirth', 'Trend_sdg5_vabirth',
+        ],
+        6:[
+            'Goal_6_Rating', 'Goal_6_Trend', 'Value_sdg6_safewater', 'Year_sdg6_safewater',
+            'Score_sdg6_safewater', 'Rating_sdg6_safewater', 'Trend_sdg6_safewater', 'Value_sdg6_sanitation',
+            'Year_sdg6_sanitation', 'Score_sdg6_sanitation', 'Rating_sdg6_sanitation', 'Trend_sdg6_sanitation',
+            'Value_sdg6_freshwater', 'Year_sdg6_freshwater', 'Score_sdg6_freshwater', 'Rating_sdg6_freshwater',
+            'Trend_sdg6_freshwater', 'Value_sdg6_ecosystems', 'Year_sdg6_ecosystems', 'Score_sdg6_ecosystems',
+            'Rating_sdg6_ecosystems', 'Trend_sdg6_ecosystems', 'Value_sdg6_wateruse', 'Year_sdg6_wateruse',
+            'Score_sdg6_wateruse', 'Rating_sdg6_wateruse', 'Trend_sdg6_wateruse', 'Value_sdg6_waterstress',
+            'Year_sdg6_waterstress', 'Score_sdg6_waterstress', 'Rating_sdg6_waterstress', 'Trend_sdg6_waterstress',
+            'Value_sdg6_quality', 'Year_sdg6_quality', 'Score_sdg6_quality', 'Rating_sdg6_quality', 'Trend_sdg6_quality',
+            'Value_sdg6_wastewater', 'Year_sdg6_wastewater', 'Score_sdg6_wastewater', 'Rating_sdg6_wastewater',
+            'Trend_sdg6_wastewater', 'Value_sdg6_river', 'Year_sdg6_river', 'Score_sdg6_river', 'Rating_sdg6_river',
+            'Trend_sdg6_river', 'Value_sdg6_fisheries', 'Year_sdg6_fisheries', 'Score_sdg6_fisheries',
+            'Rating_sdg6_fisheries', 'Trend_sdg6_fisheries',
+        ],
+        7:[
+            'Goal_7_Rating', 'Goal_7_Trend', 'Value_sdg7_access', 'Year_sdg7_access',
+            'Score_sdg7_access', 'Rating_sdg7_access', 'Trend_sdg7_access', 'Value_sdg7_electrify',
+            'Year_sdg7_electrify', 'Score_sdg7_electrify', 'Rating_sdg7_electrify', 'Trend_sdg7_electrify',
+            'Value_sdg7_renewcon', 'Year_sdg7_renewcon', 'Score_sdg7_renewcon', 'Rating_sdg7_renewcon',
+            'Trend_sdg7_renewcon', 'Value_sdg7_afford', 'Year_sdg7_afford', 'Score_sdg7_afford',
+            'Rating_sdg7_afford', 'Trend_sdg7_afford', 'Value_sdg7_cleancook', 'Year_sdg7_cleancook',
+            'Score_sdg7_cleancook', 'Rating_sdg7_cleancook', 'Trend_sdg7_cleancook', 'Value_sdg7_nonrenewcon',
+            'Year_sdg7_nonrenewcon', 'Score_sdg7_nonrenewcon', 'Rating_sdg7_nonrenewcon',
+            'Trend_sdg7_nonrenewcon', 'Value_sdg7_biofuel', 'Year_sdg7_biofuel', 'Score_sdg7_biofuel',
+            'Rating_sdg7_biofuel', 'Trend_sdg7_biofuel',
+        ],
+        8:[
+            'Goal_8_Rating', 'Goal_8_Trend', 'Value_sdg8_adjgrowth', 'Year_sdg8_adjgrowth',
+            'Score_sdg8_adjgrowth', 'Rating_sdg8_adjgrowth', 'Value_sdg8_slavery', 'Year_sdg8_slavery',
+            'impute_sdg8_slavery', 'Score_sdg8_slavery', 'Rating_sdg8_slavery', 'Value_sdg8_accounts',
+            'Year_sdg8_accounts', 'Score_sdg8_accounts', 'Rating_sdg8_accounts', 'Trend_sdg8_accounts',
+            'Value_sdg8_unemp', 'Year_sdg8_unemp', 'Score_sdg8_unemp', 'Rating_sdg8_unemp',
+            'Trend_sdg8_unemp',
+        ],
+        9:[
+            'Goal_9_Rating', 'Goal_9_Trend', 'Value_sdg9_roads', 'Year_sdg9_roads',
+            'Score_sdg9_roads', 'Rating_sdg9_roads', 'Value_sdg9_intuse', 'Year_sdg9_intuse',
+            'Score_sdg9_intuse', 'Rating_sdg9_intuse', 'Trend_sdg9_intuse', 'Value_sdg9_mobuse',
+            'Year_sdg9_mobuse', 'Score_sdg9_mobuse', 'Rating_sdg9_mobuse', 'Trend_sdg9_mobuse',
+            'Value_sdg9_lpi', 'Year_sdg9_lpi', 'Score_sdg9_lpi', 'Rating_sdg9_lpi', 'Trend_sdg9_lpi',
+            'Value_sdg9_uni', 'Year_sdg9_uni', 'impute_sdg9_uni', 'Score_sdg9_uni', 'Rating_sdg9_uni',
+            'Value_sdg9_articles', 'Year_sdg9_articles', 'Score_sdg9_articles', 'Rating_sdg9_articles',
+            'Trend_sdg9_articles', 'Value_sdg9_rdex', 'Year_sdg9_rdex', 'impute_sdg9_rdex', 'Score_sdg9_rdex',
+            'Rating_sdg9_rdex', 'Trend_sdg9_rdex', 'Value_sdg9_rdres', 'Year_sdg9_rdres',
+            'Rating_sdg9_rdres', 'Trend_sdg9_rdres', 'Value_sdg9_patents', 'Year_sdg9_patents',
+            'Rating_sdg9_patents', 'Trend_sdg9_patents', 'Value_sdg9_netacc', 'Year_sdg9_netacc',
+            'Rating_sdg9_netacc', 'Trend_sdg9_netacc', 'Value_sdg9_womensci', 'Year_sdg9_womensci',
+            'Rating_sdg9_womensci', 'Trend_sdg9_womensci',
+        ],
+        10:[
+            'Goal_10_Rating', 'Goal_10_Trend', 'Value_sdg10_gini', 'Year_sdg10_gini',
+            'Score_sdg10_gini', 'Rating_sdg10_gini', 'Trend_sdg10_gini', 'Value_sdg10_palma',
+            'Year_sdg10_palma', 'Score_sdg10_palma', 'Rating_sdg10_palma', 'Trend_sdg10_palma',
+            'Value_sdg10_elder', 'Year_sdg10_elder', 'Rating_sdg10_elder', 'Trend_sdg10_elder',
+        ],
+        11:[
+            'Goal_11_Rating', 'Goal_11_Trend', 'Value_sdg11_slums', 'Year_sdg11_slums',
+            'impute_sdg11_slums', 'Score_sdg11_slums', 'Rating_sdg11_slums', 'Trend_sdg11_slums',
+            'Value_sdg11_pm25', 'Year_sdg11_pm25', 'Score_sdg11_pm25', 'Rating_sdg11_pm25',
+            'Trend_sdg11_pm25', 'Value_sdg11_pipedwat', 'Year_sdg11_pipedwat',
+            'Score_sdg11_pipedwat', 'Rating_sdg11_pipedwat', 'Trend_sdg11_pipedwat',
+            'Value_sdg11_transport', 'Year_sdg11_transport', 'Score_sdg11_transport',
+            'Rating_sdg11_transport', 'Trend_sdg11_transport', 'Value_sdg11_rentover',
+            'Year_sdg11_rentover', 'Rating_sdg11_rentover', 'Trend_sdg11_rentover',
+            'Value_sdg11_walkable', 'Year_sdg11_walkable', 'Rating_sdg11_walkable',
+        ],
+        12:[
+            'Goal_12_Rating', 'Goal_12_Trend', 'Value_sdg12_waste', 'Year_sdg12_waste',
+            'impute_sdg12_waste', 'Score_sdg12_waste', 'Rating_sdg12_waste', 'Trend_sdg12_waste',
+            'Value_sdg12_recycling', 'Year_sdg12_recycling', 'impute_sdg12_recycling',
+            'Score_sdg12_recycling', 'Rating_sdg12_recycling', 'Trend_sdg12_recycling',
+            'Value_sdg12_solidwaste', 'Year_sdg12_solidwaste', 'impute_sdg12_solidwaste',
+            'Score_sdg12_solidwaste', 'Rating_sdg12_solidwaste', 'Trend_sdg12_solidwaste',
+        ],
+        13:[
+            'Goal_13_Rating', 'Goal_13_Trend', 'Value_sdg13_emissions', 'Year_sdg13_emissions',
+            'impute_sdg13_emissions', 'Score_sdg13_emissions', 'Rating_sdg13_emissions',
+            'Trend_sdg13_emissions', 'Value_sdg13_forests', 'Year_sdg13_forests',
+            'impute_sdg13_forests', 'Score_sdg13_forests', 'Rating_sdg13_forests',
+            'Trend_sdg13_forests',
+        ],
+        14:[
+            'Goal_14_Rating', 'Goal_14_Trend', 'Value_sdg14_overfishing', 'Year_sdg14_overfishing',
+            'impute_sdg14_overfishing', 'Score_sdg14_overfishing', 'Rating_sdg14_overfishing',
+            'Trend_sdg14_overfishing', 'Value_sdg14_mpa', 'Year_sdg14_mpa', 'impute_sdg14_mpa',
+            'Score_sdg14_mpa', 'Rating_sdg14_mpa', 'Trend_sdg14_mpa',
+        ],
+        17:[
+            'Goal_17_Rating', 'Goal_17_Trend', 'Value_sdg17_govex', 'Year_sdg17_govex',
+            'Score_sdg17_govex', 'Rating_sdg17_govex', 'Trend_sdg17_govex', 'Value_sdg17_oda',
+            'Year_sdg17_oda', 'Score_sdg17_oda', 'Rating_sdg17_oda', 'Trend_sdg17_oda',
+            'Value_sdg17_fp', 'Year_sdg17_fp', 'impute_sdg17_fp', 'Score_sdg17_fp',
+            'Rating_sdg17_fp', 'Trend_sdg17_fp',
+        ]
+    }
+
+    
+    # Get the list of columns based on the selected SDG number
+    selected_columns = ['Country_Name'] + sdg_column_mapping.get(sdg_number, [])
+    
+    # Filter data based on country and selected columns
+    selected_data = data[data['Name'] == country_name][selected_columns]
+    return selected_data
+
+

requirements.txt

@@ -0,0 +1,5 @@
+gradio==4.9.0
+gradio_client==0.7.2
+uuid==1.30
+pandas==1.3.3
+gunicorn==20.0.4