Initial upload

README.md

@@ -1,6 +1,6 @@
 ---
 title: Age Predictor
-emoji: 📊
+emoji: 🕯📊
 colorFrom: yellow
 colorTo: purple
 sdk: gradio

app.py

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+
+
+
+# Kept getting "No module named 'fastai'" from huggingface..workaround:
+# https://stackoverflow.com/a/50255019
+import subprocess
+import sys
+
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+install("fastai")
+
+from fastai.vision.all import *
+
+
+learn_resnet   = load_learner('model - resnet18.pkl')
+learn_convnext = load_learner('model - convnext_tiny - pad - 150imgs each - cleaned.pkl')
+
+categories = ('1', '4', '8', '12', '16', '20s', '30s', '40s','50s', '60s', '70s', '80s', '90s')
+
+
+
+def classify_image_resnet(img):
+    return classify_image(learn_resnet, img)
+    
+def classify_image_convnext(img):
+    return classify_image(learn_convnext, img)
+
+
+def classify_image(learn, img):
+    tens = tensor(img) #fix apparently needed after fastai 2.7.11 released
+    pred,idx,probs = learn.predict(tens)
+    return dict(zip(categories, map(float,probs)))
+    
+    
+    
+from fastdownload import download_url
+import os
+
+    
+
+def classify_image_url_resnet(url_text):
+    return classify_image_url(learn_resnet, url_text)
+    
+def classify_image_url_convnext(url_text):
+    return classify_image_url(learn_convnext, url_text)
+
+def classify_image_url(learn, url_text):
+    try:
+        dest = 'temp.jpg'
+        download_url(url_text, dest, show_progress=False)
+        im = Image.open(dest)
+        img = im.to_thumb(256,256)
+        #resize_images(dest, max_size=400, dest=dest)
+        os.remove(dest)
+        return classify_image(learn, img)
+    except:
+        # in case there is any error, invalid URL or invalid image, etc., not sure how Gradio will handle a runtime exception so catching it to be safe
+        return { categories[0]: 0.0, categories[1]: 0.0 }
+
+
+def classify_image_url_debug(url_text):
+    try:
+        dest = 'temp.jpg'
+        download_url(url_text, dest, show_progress=False)
+        im = Image.open(dest)
+        img = im.to_thumb(256,256)
+        #resize_images(dest, max_size=400, dest=dest)
+        os.remove(dest)
+        temp = classify_image(learn_resnet, img)
+        return "Success: " + str(temp)
+    except Exception as ex:
+        error = f"{type(ex).__name__} was raised: {ex}"
+        return error;
+
+
+
+
+
+import gradio as gr
+
+
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("Rudimentary age predictor.  No refinement, just a hacked together experiment to try multiple output classes with fast.ai.  Many ways the accuracy could be improved.  See: https://www.kaggle.com/code/zachwormgoor/age-predictor \r\n")
+    gr.Markdown("Predict age from uploaded image or from provide URL to image file:")
+    with gr.Tabs():
+        with gr.TabItem("Image - resnet18"):
+            with gr.Row():
+                img_r_input = gr.Image()
+                img_r_output = gr.outputs.Label()
+            image_r_button = gr.Button("Predict")
+        with gr.TabItem("URL - resnet18"):
+            with gr.Row():
+                text_r_input = gr.Textbox()
+                text_r_output = gr.outputs.Label()
+            url_r_button = gr.Button("Predict")
+        with gr.TabItem("Image - convnext_tiny"):
+            with gr.Row():
+                img_c_input = gr.Image()
+                img_c_output = gr.outputs.Label()
+            image_c_button = gr.Button("Predict")
+        with gr.TabItem("URL - convnext_tiny"):
+            with gr.Row():
+                text_c_input = gr.Textbox()
+                text_c_output = gr.outputs.Label()
+            text_c_button = gr.Button("Predict")
+        with gr.TabItem("URL - debug"):
+            with gr.Row():
+                text_d_input = gr.Textbox()
+                text_d_output = gr.outputs.Label()
+            text_d_button = gr.Button("Predict - debug")
+
+    image_r_button.click(classify_image_resnet,       inputs=img_r_input,  outputs=img_r_output)
+    url_r_button.click(  classify_image_url_resnet,   inputs=text_r_input, outputs=text_r_output)
+    image_c_button.click(classify_image_convnext,     inputs=img_c_input,  outputs=img_c_output)
+    text_c_button.click( classify_image_url_convnext, inputs=text_c_input, outputs=text_d_output)
+    text_d_button.click( classify_image_url_debug,    inputs=text_d_input, outputs=text_d_output)
+ 
+demo.launch()
+
+
+
+
+
+
+
+
+

model - convnext_tiny - pad - 150imgs each - cleaned.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a8ab30e8f418a5feb43a594f56b6cdfdceb8f19084f73ac3ea15a26fbc4628ac
+size 114640499

model - resnet18.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d7659296c5f6b608a344640ee5f573f9884a65ca7166107965f16c2c69152b2
+size 47021807