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.ipynb_checkpoints/app-checkpoint.py

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+# !pip install transformers==4.37.2 gradio==4.25.0
+import gradio as gr
+from transformers import pipeline
+import numpy as np
+age_classifier = pipeline("image-classification", model="nateraw/vit-age-classifier")
+emotion_classifier = pipeline("image-classification", model="jhoppanne/Image-Emotion-Classification")
+def pred_age_emotion(input_image):
+    if isinstance(input_image,np.ndarray):
+        img = Image.fromarray(input_image)
+        #age classifier
+        age_result = age_classifier(img)
+        age_score = age_result[0].get('score')
+        age_label = age_result[0].get('label')
+        txt1 =''
+        txt1 += f'The Model predict that the person in this image is around {age_label} years old.\n'
+        txt1 += f'with confident score : {age_score*100:.2f}%'
+        #emotion classifier
+        emotion_result = emotion_classifier(img)
+        emotion_score = emotion_result[0].get('score')
+        emotion_label = emotion_result[1].get('label')
+        txt2=''
+        txt2+= f'The Model predict that the emotion of person in this image is {emotion_label}.\n'
+        txt2+= f'with confident score : {emotion_score*100:.2f}% '
+    else:
+        txt1,txt2 =  "sorry, unable to process the image"
+    return txt1, txt2
+    # return f"Data type of uploaded image: {type(img)}"
+def pred_emotion(input_image):
+    return
+iface = gr.Interface(fn=pred_age_emotion, inputs = gr.Image(), outputs = ["text", "text"])
+iface.launch(share=True)

app.py

@@ -1,6 +1,7 @@
 # !pip install transformers==4.37.2 gradio==4.25.0
 import gradio as gr
 from transformers import pipeline
+import numpy as np
 age_classifier = pipeline("image-classification", model="nateraw/vit-age-classifier")
 emotion_classifier = pipeline("image-classification", model="jhoppanne/Image-Emotion-Classification")
 def pred_age_emotion(input_image):