feat: add basic api with really bad model

Refs: OPS-85

pyproject.toml

@@ -2,9 +2,12 @@
 name = "senju"
 version = "0.1.0"
 description = "API / Webservice for Phrases/Words/Kanji/Haiku"
-authors = [{ name = "Christoph J. Scherr", email = "software@cscherr.de" },{name = "Moritz Marquard", email="mrmarquard@protonmail.com"}]
+authors = [
+	{ name = "Christoph J. Scherr", email = "software@cscherr.de" },
+	{ name = "Moritz Marquard", email = "mrmarquard@protonmail.com" },
+]
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.10,<3.13"
 dependencies = [
 	"jinja2 (>=3.1.5,<4.0.0)",
 	"pytest>=7.0.0",
@@ -13,6 +16,8 @@ dependencies = [
 	"requests (>=2.32.3,<3.0.0)",
 	"coverage (>=7.6.12,<8.0.0)",
 	"pytest-httpserver (>=1.1.2,<2.0.0)",
+	"pillow (>=11.1.0,<12.0.0)",
+	"tensorflow (>=2.19.0,<3.0.0)",
 ]
 
 

senju/image_reco.py

@@ -0,0 +1,40 @@
+import numpy as np
+from PIL import Image
+import keras
+import io
+
+g_model = None
+
+
+class SimpleClassifier:
+    def __init__(self):
+        global g_model
+        if g_model is None:
+            g_model = keras.applications.MobileNetV2(weights="imagenet")
+        self.model = g_model
+
+    def classify(self, image_data):
+        # Convert uploaded bytes to image
+        img = Image.open(io.BytesIO(image_data)).convert("RGB")
+        img = img.resize((224, 224))
+        img_array = np.array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+
+        # Get predictions
+        predictions = self.model.predict(img_array)
+        results = keras.applications.mobilenet_v2.decode_predictions(
+            predictions, top=5)[0]
+
+        data: dict = {}
+        all_labels: list[dict] = []
+        data["best_guess"] = {"label": "", "confidence": float(0)}
+        for _, label, score in results:
+            score = float(score)
+            datapoint = {"label": label, "confidence": score}
+            all_labels.append(datapoint)
+            if data["best_guess"]["confidence"] < score:
+                data["best_guess"] = datapoint
+
+        data["all"] = all_labels
+
+        return data

senju/main.py

@@ -6,6 +6,7 @@ from flask import (Flask, redirect, render_template, request, url_for,
                    send_from_directory)
 
 from senju.haiku import Haiku
+from senju.image_reco import SimpleClassifier
 from senju.store_manager import StoreManager
 
 import os
@@ -60,6 +61,23 @@ def scan_view():
     )
 
 
+@app.route("/api/v1/image_reco", methods=['POST'])
+def image_recognition():
+    # note that the classifier is a singleton
+    classifier = SimpleClassifier()
+    if 'image' not in request.files:
+        return "No image file provided", 400
+
+    image_file = request.files['image']
+    image_data = image_file.read()
+
+    try:
+        results = classifier.classify(image_data)
+        return results
+    except Exception as e:
+        return str(e), 500
+
+
 @app.route("/api/v1/haiku", methods=['POST'])
 def generate_haiku():
     if request.method == 'POST':

senju/static/js/scan.js

@@ -56,15 +56,46 @@ function handleSubmit() {
     // Hide error
     errorMessage.classList.add("hidden");
 
-    // Show response box
+    // Show loading state
+    document.getElementById("ai-response").textContent = "Analyzing image...";
     responseBox.classList.remove("opacity-0");
 
-    // Example response
+    // Get the file from the input
-    document.getElementById("ai-response").textContent =
+    const file = dropzoneFile.files[0];
-      "Dominic Monaghan interviewing Elijah Wood if he will wear wigs";
+
+    // Create FormData object to send the file
+    const formData = new FormData();
+    formData.append("image", file);
+
+    // Send the image to your backend API
+    fetch("/api/v1/image_reco", {
+      method: "POST",
+      body: formData,
+    })
+      .then((response) => {
+        if (!response.ok) {
+          throw new Error("Network response was not ok");
+        }
+        return response.json();
+      })
+      .then((data) => {
+        // Extract top result and display it
+        if (data.results && data.results.length > 0) {
+          const topResult = data.results[0];
+          document.getElementById("ai-response").textContent =
+            `${topResult.label} (${Math.round(topResult.confidence * 100)}% confidence)`;
+        } else {
+          document.getElementById("ai-response").textContent =
+            "Could not identify the image";
+        }
+      })
+      .catch((error) => {
+        console.error("Error:", error);
+        document.getElementById("ai-response").textContent =
+          "Error analyzing image";
+      });
   } else {
     errorMessage.classList.remove("hidden");
-
     uploadArea.classList.add("shake");
     setTimeout(() => {
       uploadArea.classList.remove("shake");