updated

app.py

@@ -1,7 +1,111 @@
 import gradio as gr
+from transformers import pipeline
+from PIL import Image
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Function to classify the face shape
+def classify_face_shape(image):
+    
+    # Initialize the pipeline
+    pipe = pipeline("image-classification", model="metadome/face_shape_classification")
+    
+    # Run the pipeline on the uploaded image
+    output = pipe(image)
+    # Log the output for debugging
+    print("Pipeline output for shape:", output)
+    # Format the output to be compatible with gr.outputs.Label
+    formatted_output = {item['label']: item['score'] for item in output}
+    
+    return formatted_output
+    
+def classify_age(image):
+    pipe = pipeline("image-classification", model="nateraw/vit-age-classifier")
+    # Run the pipeline on the uploaded image
+    output = pipe(image)
+    
+    print("Pipeline output for age:", output)
+    # Format the output to be compatible with gr.outputs.Label
+    formatted_output = {item['label']: item['score'] for item in output}
+    
+    return formatted_output
+    
+def classify_skin_type(image):
+    pipe = pipeline("image-classification", model="dima806/skin_types_image_detection")
+    
+    # Run the pipeline on the uploaded image
+    output = pipe(image)
+    
+    print("Pipeline output for skin_type:", output)
+    # Format the output to be compatible with gr.outputs.Label
+    formatted_output = {item['label']: item['score'] for item in output}
+    
+    return formatted_output
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+def classify_acne_type(image):
+    pipe = pipeline("image-classification", model="imfarzanansari/skintelligent-acne")
+    
+    # Run the pipeline on the uploaded image
+    output = pipe(image)
+    
+    print("Pipeline output for acne:", output)
+    # Format the output to be compatible with gr.outputs.Label
+    formatted_output = {item['label']: item['score'] for item in output}
+    
+    return formatted_output
+
+def classify_hair_color(image):
+    
+    #pipe = pipeline("image-classification", model="enzostvs/hair-color")
+    pipe = pipeline("image-classification", model="londe33/hair_v02")
+    
+    # Run the pipeline on the uploaded image
+    output = pipe(image)
+    
+    print("Pipeline output for hir color:", output)
+    # Format the output to be compatible with gr.outputs.Label
+    formatted_output = {item['label']: item['score'] for item in output}
+    
+    return formatted_output
+
+def classify_eye_shape(image):
+    
+    pipe = pipeline("image-classification", model="justingrammens/eye-shape")
+    
+    # Run the pipeline on the uploaded image
+    output = pipe(image)
+    
+    print("Pipeline output for eye shape:", output)
+    # Format the output to be compatible with gr.outputs.Label
+    formatted_output = {item['label']: item['score'] for item in output}
+    
+    return formatted_output
+    
+    
+def classify_image_with_multiple_models(image):
+    face_shape_result = classify_face_shape(image)
+    age_result = classify_age(image)
+    skin_type_result = classify_skin_type(image)
+    acne_results = classify_acne_type(image)
+    hair_color_results = classify_hair_color(image)
+    eye_shape = classify_eye_shape(image)
+
+    return face_shape_result, age_result, skin_type_result, acne_results, hair_color_results, eye_shape
+
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=classify_image_with_multiple_models,  # The function to run
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=5, label="Face Shape"), 
+        gr.Label(num_top_classes=5, label="Age"), 
+        gr.Label(num_top_classes=3, label="Skin Type"), 
+        gr.Label(num_top_classes=5, label="Acne Type"), 
+        gr.Label(num_top_classes=5, label="Hair Color"), 
+        gr.Label(num_top_classes=4, label="Eye Shape")
+    ],
+    title="Multiple Model Classification",
+    description="Upload an image to classify the face using mutiple classification models"
+)
+    
 demo.launch(auth=("admin", "pass1234"))
+#demo.launch()