Upload app.py

app.py

@@ -0,0 +1,498 @@
+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., <instance>.<cloud>.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
+                                }
+                                
+
+                                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("https://us-central1-dbt-prod.cloudfunctions.net/deploy-databricks-pipeline", 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="🚫")
+