app.py

import streamlit as st
import requests
import re
import json
import time
import pandas as pd
import labelbox

def validate_dataset_name(name):
    """Validate the dataset name."""
    # Check length
    if len(name) > 256:
        return "Dataset name should be limited to 256 characters."
    # Check allowed characters
    allowed_characters_pattern = re.compile(r'^[A-Za-z0-9 _\-.,()\/]+$')
    if not allowed_characters_pattern.match(name):
        return ("Dataset name can only contain letters, numbers, spaces, and the following punctuation symbols: _-.,()/. Other characters are not supported.")
    return None

def create_new_dataset_labelbox (new_dataset_name):
    client = labelbox.Client(api_key=labelbox_api_key)
    dataset_name = new_dataset_name
    dataset = client.create_dataset(name=dataset_name)
    dataset_id = dataset.uid
    return dataset_id


def get_dataset_from_labelbox(labelbox_api_key):
    client = labelbox.Client(api_key=labelbox_api_key)
    datasets = client.get_datasets()
    return datasets

def destroy_databricks_context(cluster_id, context_id, domain, databricks_api_key):
    DOMAIN = f"https://{domain}"
    TOKEN = f"Bearer {databricks_api_key}"

    headers = {
        "Authorization": TOKEN,
        "Content-Type": "application/json",
    }

    # Destroy context
    destroy_payload = {
        "clusterId": cluster_id,
        "contextId": context_id
    }
    destroy_response = requests.post(
        f"{DOMAIN}/api/1.2/contexts/destroy",
        headers=headers,
        data=json.dumps(destroy_payload)
    )

    if destroy_response.status_code != 200:
        raise ValueError("Failed to destroy context.")
    
def execute_databricks_query(query, cluster_id, domain, databricks_api_key):
    DOMAIN = f"https://{domain}"
    TOKEN = f"Bearer {databricks_api_key}"

    headers = {
        "Authorization": TOKEN,
        "Content-Type": "application/json",
    }

    # Create context
    context_payload = {
        "clusterId": cluster_id,
        "language": "sql"
    }
    context_response = requests.post(
        f"{DOMAIN}/api/1.2/contexts/create",
        headers=headers,
        data=json.dumps(context_payload)
    )
    context_response_data = context_response.json()

    if 'id' not in context_response_data:
        raise ValueError("Failed to create context.")
    context_id = context_response_data['id']

    # Execute query
    command_payload = {
        "clusterId": cluster_id,
        "contextId": context_id,
        "language": "sql",
        "command": query
    }
    command_response = requests.post(
        f"{DOMAIN}/api/1.2/commands/execute",
        headers=headers,
        data=json.dumps(command_payload)
    ).json()

    if 'id' not in command_response:
        raise ValueError("Failed to execute command.")
    command_id = command_response['id']

    # Wait for the command to complete
    while True:
        status_response = requests.get(
            f"{DOMAIN}/api/1.2/commands/status",
            headers=headers,
            params={
                "clusterId": cluster_id,
                "contextId": context_id,
                "commandId": command_id
            }
        ).json()

        command_status = status_response.get("status")

        if command_status == "Finished":
            break
        elif command_status in ["Error", "Cancelled"]:
            raise ValueError(f"Command {command_status}. Reason: {status_response.get('results', {}).get('summary')}")
        else:
            time.sleep(1)  # Wait for 5 seconds before checking again

    # Convert the results into a pandas DataFrame
    data = status_response.get('results', {}).get('data', [])
    columns = [col['name'] for col in status_response.get('results', {}).get('schema', [])]
    df = pd.DataFrame(data, columns=columns)

    destroy_databricks_context(cluster_id, context_id, domain, databricks_api_key)
    
    return df


st.title("Labelbox 🤝 Databricks")
st.header("Pipeline Creator", divider='rainbow')



def is_valid_url_or_uri(value):
    """Check if the provided value is a valid URL or URI."""
    # Check general URLs
    url_pattern = re.compile(
        r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
    )
    
    # Check general URIs including cloud storage URIs (like gs://, s3://, etc.)
    uri_pattern = re.compile(
        r'^(?:[a-z][a-z0-9+.-]*:|/)(?:/?[^\s]*)?$|^(gs|s3|azure|blob)://[^\s]+'
    )
    
    return url_pattern.match(value) or uri_pattern.match(value)



is_preview = st.toggle('Run in Preview Mode', value=False)
if is_preview:
    st.success('Running in Preview mode!', icon="✅")
else:
    st.success('Running in Production mode!', icon="✅")

st.subheader("Tell us about your Databricks and Labelbox environments", divider='grey')
title = st.text_input('Enter Databricks Domain (e.g., 3980281744248452.2.gcp.databricks.com)', '')
databricks_api_key = st.text_input('Databricks API Key', type='password')
labelbox_api_key = st.text_input('Labelbox API Key', type='password')

# After Labelbox API key is entered
if labelbox_api_key:
    # Fetching datasets
    datasets = get_dataset_from_labelbox(labelbox_api_key)
    create_new_dataset = st.toggle("Make me a new dataset", value=False)

    if not create_new_dataset:
        # The existing logic for selecting datasets goes here.
        dataset_name_to_id = {dataset.name: dataset.uid for dataset in datasets}
        selected_dataset_name = st.selectbox("Select an existing dataset:", list(dataset_name_to_id.keys()))
        dataset_id = dataset_name_to_id[selected_dataset_name]

    else:
        # If user toggles "make me a new dataset"
        new_dataset_name = st.text_input("Enter the new dataset name:")

        # Check if the name is valid
        if new_dataset_name:
            validation_message = validate_dataset_name(new_dataset_name)
            if validation_message:
                st.error(validation_message, icon="🚫")
            else:
                st.success(f"Valid dataset name! Dataset_id", icon="✅")
                dataset_name = new_dataset_name

# Define the variables beforehand with default values (if not defined)
new_dataset_name = new_dataset_name if 'new_dataset_name' in locals() else None
selected_dataset_name = selected_dataset_name if 'selected_dataset_name' in locals() else None

if new_dataset_name or selected_dataset_name:
    # Handling various formats of input
    formatted_title = re.sub(r'^https?://', '', title)  # Remove http:// or https://
    formatted_title = re.sub(r'/$', '', formatted_title)  # Remove trailing slash if present

    if formatted_title:
        st.subheader("Select and existing cluster or make a new one", divider='grey', help="Jobs in preview mode will use all purpose compute clusters to help you itersate faster. Jobs in production mode will use job clusters to reduce DBUs consumed.")
        DOMAIN = f"https://{formatted_title}"
        TOKEN = f"Bearer {databricks_api_key}"

        HEADERS = {
            "Authorization": TOKEN,
            "Content-Type": "application/json",
        }

        # Endpoint to list clusters
        ENDPOINT = "/api/2.0/clusters/list"

        try:
            response = requests.get(DOMAIN + ENDPOINT, headers=HEADERS)
            response.raise_for_status()

            # Include clusters with cluster_source "UI" or "API"
            clusters = response.json().get("clusters", [])
            cluster_dict = {
                cluster["cluster_name"]: cluster["cluster_id"]
                for cluster in clusters if cluster.get("cluster_source") in ["UI", "API"]
            }

            # Display dropdown with cluster names
            make_cluster = st.toggle('Make me a new cluster', value=False)
            if make_cluster:
                #make a cluster
                st.write("Making a new cluster")
            else:
                if cluster_dict:
                    selected_cluster_name = st.selectbox(
                        'Select a cluster to run on',
                        list(cluster_dict.keys()),
                        key='unique_key_for_cluster_selectbox',
                        index=None,
                        placeholder="Select a cluster..",
                    )
                    if selected_cluster_name:
                        cluster_id = cluster_dict[selected_cluster_name]
                else:
                    st.write("No UI or API-based compute clusters found.")

        except requests.RequestException as e:
            st.write(f"Error communicating with Databricks API: {str(e)}")
        except ValueError:
            st.write("Received unexpected response from Databricks API.")

        if selected_cluster_name and cluster_id:
            # Check if the selected cluster is running
            cluster_state = [cluster["state"] for cluster in clusters if cluster["cluster_id"] == cluster_id][0]

            # If the cluster is not running, start it
            if cluster_state != "RUNNING":
                with st.spinner("Starting the selected cluster. This typically takes 10 minutes. Please wait..."):
                    start_response = requests.post(f"{DOMAIN}/api/2.0/clusters/start", headers=HEADERS, json={"cluster_id": cluster_id})
                    start_response.raise_for_status()

                    # Poll until the cluster is up or until timeout
                    start_time = time.time()
                    timeout = 1200  # 20 minutes in seconds
                    while True:
                        cluster_response = requests.get(f"{DOMAIN}/api/2.0/clusters/get", headers=HEADERS, params={"cluster_id": cluster_id}).json()
                        if "state" in cluster_response:
                            if cluster_response["state"] == "RUNNING":
                                break
                            elif cluster_response["state"] in ["TERMINATED", "ERROR"]:
                                st.write(f"Error starting cluster. Current state: {cluster_response['state']}")
                                break

                        if (time.time() - start_time) > timeout:
                            st.write("Timeout reached while starting the cluster.")
                            break

                        time.sleep(10)  # Check every 10 seconds

                st.success(f"{selected_cluster_name} is now running!", icon="🏃‍♂️")
            else:
                st.success(f"{selected_cluster_name} is already running!", icon="🏃‍♂️")


            def generate_cron_expression(freq, hour=0, minute=0, day_of_week=None, day_of_month=None):
                """
                Generate a cron expression based on the provided frequency and time.
                """
                if freq == "1 minute":
                    return "0 * * * * ?"
                elif freq == "1 hour":
                    return f"0 {minute} * * * ?"
                elif freq == "1 day":
                    return f"0 {minute} {hour} * * ?"
                elif freq == "1 week":
                    if not day_of_week:
                        raise ValueError("Day of week not provided for weekly frequency.")
                    return f"0 {minute} {hour} ? * {day_of_week}"
                elif freq == "1 month":
                    if not day_of_month:
                        raise ValueError("Day of month not provided for monthly frequency.")
                    return f"0 {minute} {hour} {day_of_month} * ?"
                else:
                    raise ValueError("Invalid frequency provided")

            # Streamlit UI
            st.subheader("Run Frequency", divider='grey')

            # Dropdown to select frequency
            freq_options = ["1 minute", "1 hour", "1 day", "1 week", "1 month"]
            selected_freq = st.selectbox("Select frequency:", freq_options, placeholder="Select frequency..")

            day_of_week = None
            day_of_month = None

            # If the frequency is hourly, daily, weekly, or monthly, ask for a specific time
            if selected_freq != "1 minute":
                col1, col2 = st.columns(2)
                with col1:
                    hour = st.selectbox("Hour:", list(range(0, 24)))
                with col2:
                    minute = st.selectbox("Minute:", list(range(0, 60)))
                
                if selected_freq == "1 week":
                    days_options = ["MON", "TUE", "WED", "THU", "FRI", "SAT", "SUN"]
                    day_of_week = st.selectbox("Select day of the week:", days_options)

                elif selected_freq == "1 month":
                    day_of_month = st.selectbox("Select day of the month:", list(range(1, 32)))

            else:
                hour, minute = 0, 0

            # Generate the cron expression
            frequency = generate_cron_expression(selected_freq, hour, minute, day_of_week, day_of_month)

            def generate_human_readable_message(freq, hour=0, minute=0, day_of_week=None, day_of_month=None):
                """
                Generate a human-readable message for the scheduling.
                """
                if freq == "1 minute":
                    return "Job will run every minute."
                elif freq == "1 hour":
                    return f"Job will run once an hour at minute {minute}."
                elif freq == "1 day":
                    return f"Job will run daily at {hour:02}:{minute:02}."
                elif freq == "1 week":
                    if not day_of_week:
                        raise ValueError("Day of week not provided for weekly frequency.")
                    return f"Job will run every {day_of_week} at {hour:02}:{minute:02}."
                elif freq == "1 month":
                    if not day_of_month:
                        raise ValueError("Day of month not provided for monthly frequency.")
                    return f"Job will run once a month on day {day_of_month} at {hour:02}:{minute:02}."
                else:
                    raise ValueError("Invalid frequency provided")

            # Generate the human-readable message
            readable_msg = generate_human_readable_message(selected_freq, hour, minute, day_of_week, day_of_month)

            if frequency:
                st.success(readable_msg, icon="📅")

                st.subheader("Select a table", divider="grey")

                with st.spinner('Querying Databricks...'):
                    query = "SHOW DATABASES;"
                    result_data = execute_databricks_query(query, cluster_id, formatted_title, databricks_api_key)

                    # Extract the databaseName values from the DataFrame
                    database_names = result_data['databaseName'].tolist()

                # Create a dropdown with the database names
                selected_database = st.selectbox("Select a Database:", database_names, index=None, placeholder="Select a database..")

                if selected_database:
                    with st.spinner('Querying Databricks...'):
                        query = f"SHOW TABLES IN {selected_database};"
                        result_data = execute_databricks_query(query, cluster_id, formatted_title, databricks_api_key)

                        # Extract the tableName values from the DataFrame
                        table_names = result_data['tableName'].tolist()

                    # Create a dropdown with the database names
                    selected_table = st.selectbox("Select a Table:", table_names, index=None, placeholder="Select a table..")

                    if selected_table:
                        with st.spinner('Querying Databricks...'):
                            query = f"SHOW COLUMNS IN {selected_database}.{selected_table};"
                            result_data = execute_databricks_query(query, cluster_id, formatted_title, databricks_api_key)
                            column_names = result_data['col_name'].tolist()

                        st.subheader("Map table schema to Labelbox schema", divider="grey")
                        # Your existing code to handle schema mapping...

                        # Fetch the first 5 rows of the selected table
                        with st.spinner('Fetching first 5 rows of the selected table...'):
                            query = f"SELECT * FROM {selected_database}.{selected_table} LIMIT 5;"
                            table_sample_data = execute_databricks_query(query, cluster_id, formatted_title, databricks_api_key)

                        # Display the sample data in the Streamlit UI
                        st.write(table_sample_data)

                        
                        # Define two columns for side-by-side selectboxes
                        col1, col2 = st.columns(2)

                        with col1:
                            selected_row_data = st.selectbox(
                                "row_data (required):", 
                                column_names, 
                                index=None, 
                                placeholder="Select a column..", 
                                help="Select the column that contains the URL/URI bucket location of the data rows you wish to import into Labelbox."
                            )

                        with col2:
                            selected_global_key = st.selectbox(
                                "global_key (optional):", 
                                column_names, 
                                index=None, 
                                placeholder="Select a column..", 
                                help="Select the column that contains the global key. If not provided, a new key will be generated for you."
                            )

                        # Fetch a single row from the selected table
                        query_sample_row = f"SELECT * FROM {selected_database}.{selected_table} LIMIT 1;"
                        result_sample = execute_databricks_query(query_sample_row, cluster_id, formatted_title, databricks_api_key)
                        
                        if selected_row_data:
                            # Extract the value from the selected row_data column
                            sample_row_data_value = result_sample[selected_row_data].iloc[0]

                            # Validate the extracted value
                            if is_valid_url_or_uri(sample_row_data_value):
                                st.success(f"Sample URI/URL from selected row data column: {sample_row_data_value}", icon="✅")
                                dataset_id = create_new_dataset_labelbox(new_dataset_name) if create_new_dataset else dataset_id
                                # Mode
                                mode = "preview" if is_preview else "production"

                                # Databricks instance and API key
                                databricks_instance = formatted_title
                                databricks_api_key = databricks_api_key

                                # Dataset ID and New Dataset
                                new_dataset = 1 if create_new_dataset else 0
                                dataset_id = dataset_id 

                                # Table Path
                                table_path = f"{selected_database}.{selected_table}"
                                # Frequency
                                frequency = frequency

                                # Cluster ID and New Cluster
                                new_cluster = 1 if make_cluster else 0
                                cluster_id = cluster_id if not make_cluster else ""

                                # Schema Map
                                row_data_input = selected_row_data
                                global_key_input = selected_global_key
                                schema_map_dict = {'row_data': row_data_input}
                                if global_key_input:
                                    schema_map_dict['global_key'] = global_key_input

                                # Convert the dict to a stringified JSON
                                schema_map_str = json.dumps(schema_map_dict)
                                
                        
                                data = {
                                    "mode": mode,
                                    "databricks_instance": databricks_instance,
                                    "databricks_api_key": databricks_api_key,
                                    "new_dataset": new_dataset,
                                    "dataset_id": dataset_id,
                                    "table_path": table_path,
                                    "labelbox_api_key": labelbox_api_key,
                                    "frequency": frequency,
                                    "new_cluster": new_cluster,
                                    "cluster_id": cluster_id,
                                    "schema_map": schema_map_str
                                }
                                
                                # Display the constructed data using Streamlit
                                st.json(data)

                                if st.button("Deploy Pipeline!", type="primary"):
                                    # Ensure all fields are filled out
                                    required_fields = [
                                        mode, databricks_instance, databricks_api_key, new_dataset, dataset_id,
                                        table_path, labelbox_api_key, frequency, new_cluster, cluster_id, schema_map_str
                                    ]


                                    # Sending a POST request to the Flask app endpoint
                                    with st.spinner("Deploying pipeline..."):
                                        response = requests.post("http://127.0.0.1:5000/create-databricks-job", json=data)

                                    # Check if request was successful
                                    if response.status_code == 200:
                                        # Display the response using Streamlit
                                        st.balloons()
                                        st.success("Pipeline deployed successfully!", icon="🚀")
                                        st.json(response.json())
                                    else:
                                        st.error(f"Failed to deploy pipeline. Response: {response.text}", icon="🚫")

                            else:
                                st.error(f"row_data '{sample_row_data_value}' is not a valid URI or URL. Please select a different column.", icon="🚫")