It predicts!

This was a whole lot easier to implement than the Temporal CNN I'm doing 
for my PhD.....

Next task: tidy up the code, document it, and write the README

....that can wait until tomorrow though :P

src/lib/FilmPredictor.mjs

@@ -18,27 +18,26 @@ class FilmPredictor {
 	}
 	
 	async init(dirpath = null) {
-		if(!fs.existsSync(this.settings.output))
-			await fs.promises.mkdir(this.settings.output, { recursive: true, mode: 0o755 });
-		this.dir_checkpoints = path.join(this.settings.output, "checkpoints");
-		if(!fs.existsSync(this.dir_checkpoints))
-			await fs.promises.mkdir(this.dir_checkpoints, { recursive: true, mode: 0o755 });
-		
-		
 		if(dirpath !== null)
 			await this.load_model(dirpath);
-		else
+		else {
+			if(!fs.existsSync(this.settings.output))
+				await fs.promises.mkdir(this.settings.output, { recursive: true, mode: 0o755 });
+			this.dir_checkpoints = path.join(this.settings.output, "checkpoints");
+			if(!fs.existsSync(this.dir_checkpoints))
+				await fs.promises.mkdir(this.dir_checkpoints, { recursive: true, mode: 0o755 });
+			
 			this.make_model();
-		
-		this.model.summary();
+			
+			this.model.summary();
+		}
 	}
 	
 	async load_model(dirpath) {
-		if(!fs.existsSync(dirpath)) {
+		if(!fs.existsSync(dirpath))
 			throw new Error(`Error: The directory ${dirpath} doesn't exist.`);
-		}
 		console.error(`>>> Loading model from '${dirpath}'`);
-		this.model = await this.tf.loadLayersModel(`file://${dirpath}`);
+		this.model = await tf.loadLayersModel(`file://${path.resolve(dirpath, "model.json")}`);
 		console.error(`>>> Model loading complete`);
 	}
 	
@@ -119,8 +118,32 @@ class FilmPredictor {
 		})
 	}
 	
-	async predict(image) {
+	async predict(imagefilepath) {
+		if(!fs.existsSync(imagefilepath))
+			throw new Error(`Error: No file exists at '${imagefilepath}'`);
 		
+		let image_data = await fs.promises.readFile(imagefilepath);
+		
+		let imagetensor = tf.tidy(() => {
+			let image = tf.node.decodeImage(
+				image_data,
+				3 // channels
+			);
+			return image.reshape([1, ...image.shape ]);
+		})
+		let result_array = await this.model.predict(imagetensor).array();
+		return this.array2genres(result_array);
+	}
+	
+	array2genres(arr) {
+		let result = [];
+		for(let i = 0; i < arr.length; i++) {
+			if(arr[i] < 0.5)
+				continue;
+			
+			result.push(genres[i]);
+		}
+		return result;
 	}
 }
 

src/subcommands/predict/predict.mjs

@@ -1,5 +1,22 @@
 "use strict";
 
+import fs from 'fs';
+
+import FilmPredictor from '../../lib/FilmPredictor.mjs';
+
 export default async function(settings) {
+	if(!fs.existsSync(settings.input)) {
+		console.error(`Error: The input file '${settings.input}' doesn't exist (did you type it correctly?)`);
+		process.exit(1);
+	}
+	if(!fs.existsSync(settings.ai_model)) {
+		console.error(`Error: Failed to locate AI model directory at '${settings.ai_model}'.`);
+		process.exit(1);
+	}
+	
+	let model = new FilmPredictor(settings);
+	await model.init(settings.ai_model); // We're training a new model here
 	
+	let result = await model.predict(settings.input);
+	console.log(result.join("\n"));
 }