For the past few months or so, I’ve started tracking my movie watching with Letterboxd. I’m not doing a lot of reviews, mostly just logging, but I find it neat to look back and remind myself of what I’ve watched recently. You can see my profile if you’re curious, or check out my "Now" page as well. I thought it might be interesting to see if I could use my Letterboxd data along with Google Gemini as a way to suggest the next movie I should watch. I was able to build a quick workflow using the incredible Pipedream in a few minutes. Let me share with you how I did that.

What does it do? #

At a high level, my workflow does the following:

• It runs, automatically, once a week on Sunday. That was a bit arbitrary but felt like a good day to pick.
• Look at my Letterboxd profile and pluck out the most recent film I logged.
• From that, generate a prompt and ask Gemini what it would suggest.
• Take those suggestions and email them to me.

Now let’s take a look at that workflow in detail.

Step One – The Schedule #

This will be the quickest step as it was one of Pipedream’s built-in triggers – a CRON/schedule. All I had to was set it to weekly on Sunday.

Step Two – Getting My Letterboxd Data #

While Letterboxd has an API, they also have RSS feeds for people’s logs. You can find mine here: https://letterboxd.com/raymondcamden/rss/. In Pipedream, I added a built-in step that parses RSS feeds. I selected the "Merge" one even though I only had one feed and I’m not sure if there’s an action I missed specifically for one feed, but it worked easily enough – I simply gave it the RSS url.

As a quick aside, and as a reminder of why I love Pipedream, I’ve now got a serverless workflow with a custom schedule parsing RSS and I haven’t written one line of code.

Step Three – Generate My Prompt #

Now I actually do need to write code. I’m going to get the first item from the RSS feed, which looks like so in raw XML:

<item>	<title>Late Night with the Devil, 2023 - ★★★</title>	<link>https://letterboxd.com/raymondcamden/film/late-night-with-the-devil/</link>	<guid isPermaLink="false">letterboxd-watch-578751912</guid>	<pubDate>Sun, 21 Apr 2024 14:43:16 +1200</pubDate>	<letterboxd:watchedDate>2024-04-20</letterboxd:watchedDate>	<letterboxd:rewatch>No</letterboxd:rewatch>	<letterboxd:filmTitle>Late Night with the Devil</letterboxd:filmTitle>	<letterboxd:filmYear>2023</letterboxd:filmYear>	<letterboxd:memberRating>3.0</letterboxd:memberRating>	<tmdb:movieId>938614</tmdb:movieId>	<description><![CDATA[ <p><img src="https://a.ltrbxd.com/resized/film-poster/8/4/3/4/1/5/843415-late-night-with-the-devil-0-600-0-900-crop.jpg?v=b6c384f7c5"/></p> <p>Watched on Saturday April 20, 2024.</p> ]]></description>	<dc:creator>Raymond Camden</dc:creator></item>

I wanted the title, but not the title in <title> as it has extra info, but rather, the value in <letterboxed:filmTitle>. I grab that value, and generate my prompt:

export default defineComponent({  async run({ steps, $  }) {    let lastMovie = steps.get_films.$ return_value[0];    let title = lastMovie['letterboxd:filmtitle']['#'];        $ .export('title', title);        return `The last movie I watched is "$ Automating Movie Recommendations with Generative AI and Pipedream". What would you suggest I watch next?    `;  },})

Note how I both return the prompt and export the title. I need the title later in the workflow, but I wanted the main return to be the prompt. I’ve not returned multiple values from a Pipedream step before and I appreciate how easy it is. At the end of this step, I’ll be able to reference title from the step as well as $ return_value.

Step Four – Talking to Gemini #

At this point (if you read my blog), you’ve seen me demonstrate the Gemini API multiple times. In general, it’s trivial code, with the truly important bits being the prompt. A few days back, I blogged about how to get JSON results from your prompt using the response type setting and system instructions. I followed the same format for my code here:

import { GoogleGenerativeAI, HarmCategory, HarmBlockThreshold } from '@google/generative-ai';async function callGemini(text,model) {	const generationConfig = {		temperature: 0.9,		topK: 1,		topP: 1,		maxOutputTokens: 2048,		response_mime_type:'application/json'	};	const safetySettings = [		{ category: HarmCategory.HARM_CATEGORY_HARASSMENT, threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, },		{ category: HarmCategory.HARM_CATEGORY_HATE_SPEECH,	threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, },		{ category: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, },		{ category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, },	];	const parts = [    	{text},  	];	const result = await model.generateContent({		contents: [{ role: "user", parts }],		generationConfig,		safetySettings,	});	try {		if(result.response.promptFeedback && result.response.promptFeedback.blockReason) {			return { error: `Blocked for $ {result.response.promptFeedback.blockReason}` };		}		const response = result.response;		return JSON.parse(response.candidates[0].content.parts[0].text);	} catch(e) {		return {			error:e.message		}	}	}export default defineComponent({  async run({ steps, $  }) {        const MODEL_NAME = "gemini-1.5-pro-latest";    const API_KEY = process.env.GOOGLE_API_KEY;    const si = `You are an expert at movies and can make recommendations for a movie a person should watch based on their last film. Your response should be a JSON object containing an array of recommendations in this form:* title: The title of the movie.* year: The year it was released.* reason: A one sentence explanation for why the film was recommended.    `;        const genAI = new GoogleGenerativeAI(API_KEY);    const model = genAI.getGenerativeModel({ model: MODEL_NAME,    	systemInstruction: {    		parts: [{ text:si }],    		role:"model"    	} } , { apiVersion:'v1beta' });    return await callGemini(steps.generatePrompt.$ return_value,model);      },})

That’s a big block of code, but the important portion really is the system instructions I set up in the si string. I describe how Gemini should act and how it should return its results. The net result is an array that looks like so:

{	"$ return_value": [		{			"title": "Talk to Me",			"year": 2023,			"reason": "If you enjoyed the blend of horror and humor in "Late Night with the Devil", "Talk to Me" offers a similar tone with its exploration of a group of friends who discover how to conjure spirits using an embalmed hand."		},		{			"title": "The Blackening",			"year": 2023,			"reason": "For those who appreciated the satirical elements of "Late Night with the Devil", "The Blackening" provides a comedic horror experience that cleverly critiques horror movie tropes through a group of Black friends facing a killer."		},		{			"title": "Evil Dead Rise",			"year": 2023,			"reason": "If the demonic presence in "Late Night with the Devil" intrigued you, "Evil Dead Rise" delivers a thrilling continuation of the Evil Dead franchise, showcasing a fresh take on the iconic Deadites and their malevolent influence."		}	]}

Step Five – Generate Email #

As I plan on using HTML for my email, I created another code step for the sole purpose of creating an HTML string:

export default defineComponent({  async run({ steps, $  }) {    let html = `<h2>Movie Recommendations</h2><p>The last movie you watched was "$ {steps.generatePrompt.title}". I asked Google Gemini what you should watch nextand this is what it recommended:</p>    `;    for(let film of steps.generateResponse.$ return_value) {      html += ` <h3>$ {film.title} ($ {film.year})</h3> $ {film.reason}      `;    }    return html;  },})

Basically, here’s what you watched last, and here’s what to watch next. I spent maybe one minute making it look decent, but I could have done much more of course.

Step Six – Email #

The final step is trivial. Pipedream includes an "email you" type step where you provide a subject and content to email, and it emails the account holder, in this, case. You absolutely can use email services and they’ve got built-in actions for those, but I’m fine with the basic one.

The Results #

The last movie I watched was "Late Night with the Devil", and here’s what Gemini recommended:

Of those, I’ve seen two of them, and honestly, the recommendations feel spot on. While testing I got a few different recommendations including "Antlers", "The Blackening", and "Evil Dead Rise". Again, these feel like great recommendations.

I do think it might be interesting to grab the last two movies instead of one, and that way you would get a bit more variety in responses (assuming you watched different genres), but I figure this by itself is good enough. You can find the complete Pipedream source here: https://github.com/cfjedimaster/General-Pipedream-Stuff/tree/production/suggest-next-movie-p_ZJCRzAD. Let me know what you think!

Raymond Camden