🧾 In short: this notebook walks you through retrieving live data from X (formerly Twitter), applying AI for insights (from free open source models), and visualizing results in Fabric, blending practical use cases in weather monitoring with AI integration.

(This notebook can effectively be imported and run freely by anyone with a Microsoft Fabric Trial)

🔑Set Up X (formerly Twitter) Developer Access

To retrieve live tweets in this demo, you’ll first set up access to the X (Twitter) Developer API, then store your token securely in Azure Key Vault.

1) Sign in / Sign up

Go to the X Developer Portal: https://developer.x.com/en

• If you already have an account, click Sign in.
• Otherwise, click Sign up and follow the prompts.

2) Choose a plan

Select the Free plan (sufficient for this notebook) or go ahead for the paid versions.

• The Free tier typically allows 1 request per user every 15 minutes.
• A single request can return up to 100 tweets.

3) Generate a Bearer Token

In the Developer Portal:

1. Open Projects & Apps from the left navigation.
2. Select your project.
3. Click Generate a Bearer Token and copy it.

Security note: Treat tokens like passwords. Never commit them to source control and never print them in logs.

4) Store the token securely (Azure Key Vault)

Recommended: Save the Bearer Token as a secret in Azure Key Vault. Give the secret a clear name (e.g., TwitterBearer).

🚀Let's Fetch Live Tweets from BoM QLD

Now that your X Developer access is set up and your token is safely stored, it's time to retrieve the latest weather notices directly from the Bureau of Meteorology (QLD).

Check out the official X account: @BOM_Qld

Note: This code uses Python only and not PySpark. PySpark requires Spark jobs, which add overhead. For large datasets, PySpark offers better performance, but since this demo collects only ~100 rows of data, Python is sufficient.

Install the required packages (if not already installed)

#pip install tweepy

import tweepy
import pandas as pd
from notebookutils import mssparkutils
from notebookutils.visualization import display

# (Recommended Approach) Store and Get the Bearer Token secret from Azure Key Vault 
bearer_token = mssparkutils.credentials.getSecret("https://< Add Your Key Vault Here >.vault.azure.net/", "TwitterBearer")

# (Alternative Approach) Swap the above with the below and enter bearer token directly 
# bearer_token = "YOUR_BEARER_TOKEN_HERE"

# Connect to X API, authenticate with the Bearer Token and pause if you hit your X API rate limits
client = tweepy.Client(bearer_token=bearer_token, wait_on_rate_limit=True)

# Get user id 
user = client.get_user(username="BOM_Qld")

# Fetch the latest N tweets
tweets = client.get_users_tweets(
    id=user.data.id,
    max_results=100,                # Change as required (and as your X (twitter) plan allows)
    tweet_fields=["created_at", "text"]
)

# Convert to DataFrame and format date
df = pd.DataFrame([{"created_at": t.created_at, "text": t.text} for t in tweets.data])
df["created_at"] = pd.to_datetime(df["created_at"]).dt.strftime("%Y%m%d")

# Display the collected data 
display(df)

The data from X (i.e. 100 rows of tweets as specified)

🤖Using AI Models in Microsoft Fabric

AI can help us classify text into categories and summarise information for deeper insights.
We’ll now use AI models to enrich the collected tweets so that they can be visualised in Power BI more effectively.

📊AI in Microsoft Fabric

Microsoft Fabric provides built-in AI Functions, built with OpenAI models such as:

• Classify: https://learn.microsoft.com/en-us/fabric/data-science/ai-functions/classify
• Summarise: https://learn.microsoft.com/en-us/fabric/data-science/ai-functions/summarize?tabs=column-summary

⚠️However, at the time of creating this notebook, these built-in functions are not available in the Trial version of Fabric.
Therefore many interested and potential users have not been able to trial these AI functions.

🌍Open-Source Alternative (Hugging Face)

To work around this limitation, we’ll use open-source AI models from Hugging Face. In particular, Facebook (Meta) models.

• The models are downloaded once at runtime into your Fabric environment.
• Therefore your data never leaves Fabric — it is not sent to Hugging Face servers.
• This makes the approach secure for demos and learning purposes.

🛡️ A Note on Safety

These Facebook (Meta) models are widely adopted, open-source, and safe to experiment with in a demo context.
That said:

• Microsoft Fabric (paid) AI functions come with Microsoft security guarantees and would be the recommended approach for Production workflows.
• Otherwise, please be aware that there are methods to containerize open-source AI models (such as storage in Azure) if alternative models are preferred.

Let’s load the models and apply them to our dataset 👇

from transformers import pipeline

# Load AI models (download once, cached locally in Fabric runtime)
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Categories for classification
categories = ["Severe Warning", "Flood", "Heat", "Forecast", "Outlook"]

# Apply AI models to the data
df["summary"] = df["text"].apply(lambda t: summarizer(t, max_length=30, min_length=5, do_sample=False)[0]["summary_text"])
df["category"] = df["text"].apply(lambda t: classifier(t, candidate_labels=categories)["labels"][0])


# Display the dataframe showing the TWO NEW AI generated columns!
display(df)

With just those few lines of code, we get our two new AI generated summary and category columns!

You may now want the actual weather from BoM.

🌦Australian Weather Updates – Brisbane

The Bureau of Meteorology (BoM) provides updated weather information across Australia every 30 minutes.

The below notebook code fetches the latest weather data specifically for Brisbane.

Tip: To get data for other locations, visit https://www.bom.gov.au/ and find the Product and Station codes for your desired area.

import requests
import pandas as pd
import plotly.express as px

# Variables (Navigate to https://www.bom.gov.au/ to identify your desired Product and Station)
PRODUCT = "IDQ60801"
STATION = "94576"
URL = f"https://www.bom.gov.au/fwo/{PRODUCT}/{PRODUCT}.{STATION}.json"
HOURS = 3  # The last N hours to display

# Get the data
r = requests.get(URL, headers={"User-Agent": "Mozilla/5.0"}, timeout=20)
r.raise_for_status()
data = r.json().get("observations", {}).get("data", [])
df = pd.DataFrame(data)
if df.empty:
    raise RuntimeError("No data returned from BoM")

# Data cleansing - Fromat timestamps, drop duplicates, and sort columns
df["Timestamp"] = pd.to_datetime(df["local_date_time_full"], format="%Y%m%d%H%M%S", errors="coerce")
df = df.dropna(subset=["Timestamp"]).drop_duplicates("Timestamp").sort_values("Timestamp").reset_index(drop=True)

# Rename the column names for readibility
rename_map = {
    "air_temp": "Air Temp (°C)",  
    #"rel_hum": "Humidity (%)", 
    "wind_spd_kmh": "Wind (km/h)",
    "rain_trace": "Rain (mm)"
}
df = df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns})

# Limiting the display to the last N hours (per set Variable)
cutoff = df["Timestamp"].max() - pd.Timedelta(hours=HOURS)
df = df[df["Timestamp"] >= cutoff].reset_index(drop=True)

# Ensure all metric columns are numeric
for col in rename_map.values():
    if col in df.columns:
        df[col] = pd.to_numeric(df[col], errors="coerce")

# Select only numeric columns for plotting
ycols = [c for c in rename_map.values() if c in df.columns and pd.api.types.is_numeric_dtype(df[c])]

# Plot the dataframe with Plotly
fig = px.line(df, x="Timestamp", y=ycols, title=f"Brisbane ({STATION}) – Last {HOURS} Hours BoM Observations")
fig.update_layout(
    plot_bgcolor="white",
    paper_bgcolor="white",
    font=dict(color="black"),
    xaxis=dict(showgrid=True, gridcolor="lightgrey"),
    yaxis=dict(showgrid=True, gridcolor="lightgrey"),
    legend=dict(title="Metric")
)
fig.show()

The Plotly visual above is interactive.

For some who may be wondering about visualising the data within the Notebook with Power BI:

⚠️Pre-warning: QuickVisualize in Microsoft Fabric

While QuickVisualize provides a familiar Power BI-like interaction, its current functionality is extremely limited.

Power BI remains the world’s leading BI tool, but using powerbiclient inside a notebook gives only a lightweight version (useful perhaps for spinning up a quick semantic model), not the full Power BI experience.
Interesting to see how this evolves over time…

from powerbiclient import QuickVisualize, get_dataset_config

# Prepare the same data for Power BI as used for Plotly
df_pbi = df[["Timestamp"] + ycols]

# renders the quick report in the Fabric notebook cell
qv = QuickVisualize(get_dataset_config(df_pbi))
qv.set_size(500, 1400)
qv  

 
(The Notebook is attached for you to import and run)

https%3A%2F%2Fgithub.com%2F