Upload requirements.txt; app.py; mon_modele;birs.csv;tes_data.npy

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+mon_modele/mon_modele/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import load_model
+import pandas as pd
+import numpy as np
+
+dataCsv = pd.read_csv('birds.csv')#ilfaut que tu changes les chemins avec le stiens
+
+from tensorflow.keras.preprocessing import image
+
+
+def load_and_preprocess_image(img_path, target_size=(224, 224)):
+    img = image.load_img(img_path, target_size=target_size);
+    img_array = image.img_to_array(img);
+    img_array = np.expand_dims(img_array, axis=0);
+    img_array /= 255.0
+    return img_array
+
+
+def display_image_and_prediction(Oiseau):
+    model = load_model('mon_modele')
+    generator = np.load('test_data.npy')
+    img_array = load_and_preprocess_image(Oiseau);
+    prediction = model.predict(img_array);
+    predicted_class = np.argmax(prediction);
+    predicted_class_name = generator[predicted_class];
+    scientificName = dataCsv.loc[dataCsv["labels"]==predicted_class_name,"scientific name"].iloc[0]#récupération du nom scientifique de cette oiseau
+
+    return predicted_class, scientificName
+
+iface = gr.Interface(fn=display_image_and_prediction(), inputs="image", outputs=["text","text"])
+iface.launch()

mon_modele/mon_modele/fingerprint.pb

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+size 56

mon_modele/mon_modele/keras_metadata.pb

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+size 1008725

mon_modele/mon_modele/saved_model.pb

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+size 5515270

mon_modele/mon_modele/variables/variables.data-00000-of-00001

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requirements.txt

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+pandas
+numpy
+tensorflow

test_data.npy

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