minimal app

app.py

@@ -1,4 +1,179 @@
+from copy import deepcopy
+
+from huggingface_hub import from_pretrained_fastai
+from PIL import Image
 import streamlit as st
+import pandas as pd
+
+_LABELS = (
+    "affenpinscher",
+    "afghan_hound",
+    "african_hunting_dog",
+    "airedale",
+    "american_staffordshire_terrier",
+    "appenzeller",
+    "australian_terrier",
+    "basenji",
+    "basset",
+    "beagle",
+    "bedlington_terrier",
+    "bernese_mountain_dog",
+    "black-and-tan_coonhound",
+    "blenheim_spaniel",
+    "bloodhound",
+    "bluetick",
+    "border_collie",
+    "border_terrier",
+    "borzoi",
+    "boston_bull",
+    "bouvier_des_flandres",
+    "boxer",
+    "brabancon_griffon",
+    "briard",
+    "brittany_spaniel",
+    "bull_mastiff",
+    "cairn",
+    "cardigan",
+    "chesapeake_bay_retriever",
+    "chihuahua",
+    "chow",
+    "clumber",
+    "cocker_spaniel",
+    "collie",
+    "curly-coated_retriever",
+    "dandie_dinmont",
+    "dhole",
+    "dingo",
+    "doberman",
+    "english_foxhound",
+    "english_setter",
+    "english_springer",
+    "entlebucher",
+    "eskimo_dog",
+    "flat-coated_retriever",
+    "french_bulldog",
+    "german_shepherd",
+    "german_short-haired_pointer",
+    "giant_schnauzer",
+    "golden_retriever",
+    "gordon_setter",
+    "great_dane",
+    "great_pyrenees",
+    "greater_swiss_mountain_dog",
+    "groenendael",
+    "ibizan_hound",
+    "irish_setter",
+    "irish_terrier",
+    "irish_water_spaniel",
+    "irish_wolfhound",
+    "italian_greyhound",
+    "japanese_spaniel",
+    "keeshond",
+    "kelpie",
+    "kerry_blue_terrier",
+    "komondor",
+    "kuvasz",
+    "labrador_retriever",
+    "lakeland_terrier",
+    "leonberg",
+    "lhasa",
+    "malamute",
+    "malinois",
+    "maltese_dog",
+    "mexican_hairless",
+    "miniature_pinscher",
+    "miniature_poodle",
+    "miniature_schnauzer",
+    "newfoundland",
+    "norfolk_terrier",
+    "norwegian_elkhound",
+    "norwich_terrier",
+    "old_english_sheepdog",
+    "otterhound",
+    "papillon",
+    "pekinese",
+    "pembroke",
+    "pomeranian",
+    "pug",
+    "redbone",
+    "rhodesian_ridgeback",
+    "rottweiler",
+    "saint_bernard",
+    "saluki",
+    "samoyed",
+    "schipperke",
+    "scotch_terrier",
+    "scottish_deerhound",
+    "sealyham_terrier",
+    "shetland_sheepdog",
+    "shih-tzu",
+    "siberian_husky",
+    "silky_terrier",
+    "soft-coated_wheaten_terrier",
+    "staffordshire_bullterrier",
+    "standard_poodle",
+    "standard_schnauzer",
+    "sussex_spaniel",
+    "tibetan_mastiff",
+    "tibetan_terrier",
+    "toy_poodle",
+    "toy_terrier",
+    "vizsla",
+    "walker_hound",
+    "weimaraner",
+    "welsh_springer_spaniel",
+    "west_highland_white_terrier",
+    "whippet",
+    "wire-haired_fox_terrier",
+    "yorkshire_terrier",
+)
+
+
+def get_breed(path):
+    pass
+
+
+@st.cache_resource
+def get_predictor():
+    return from_pretrained_fastai("TheDima/resnet50-dog-breed-identification")
+
+
+def predict(image):
+    # Get predictions
+    predictor = get_predictor()
+    pred, pred_idx, probs = predictor.predict(image)
+    return pred, probs[pred_idx].item(), probs
+
+
+def print_probabilities(probs, labels, top_n=10):
+    df = pd.DataFrame({"Label": labels, "Probability": probs})
+    df = df.sort_values(by="Probability", ascending=False).head(top_n)
+    st.dataframe(df, column_order=["Label", "Probability"], hide_index=True)
+
+
+st.title("Dog Breed Recognition")
+
+uploaded_file = st.file_uploader("Upload a doggy...", type=["jpg", "jpeg"])
+if uploaded_file is not None:
+    # Display uploaded image
+    image = Image.open(uploaded_file)
+    image_copy = deepcopy(image)
+
+    # Make a prediction
+    with st.spinner("Checking..."):
+        pred, prob, probs = predict(image)
+
+    centered_html = f"""
+        <div style="text-align: center;">
+            <h3>It is {pred.replace("_", " ").title()}! (I am {100*prob:.1f}% sure 😉)</h3> <br>
+        </div>
+    """
+    st.markdown(centered_html, unsafe_allow_html=True)
+    st.image(image_copy, caption="Uploaded doggy", use_column_width=True)
+
+    st.markdown("---")
+    st.markdown("Nerdy Top-10 Probabilities: ")
+    print_probabilities(probs, _LABELS)
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.markdown("---")
+st.write(f"We know only 120 breeds: {', '.join(_LABELS).replace('_', ' ').title()}.")

requirements.txt

@@ -0,0 +1,4 @@
+huggingface_hub
+pandas
+pillow
+streamlit