basic ui

README.md

@@ -9,4 +9,8 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## RUN Gradio Locally
+```
+pip install gradio 
+gradio app.py
+```

app.py

@@ -1,7 +1,57 @@
 import gradio as gr
+import requests
+import random
+from src.classification_model import ClassificationModel
 
-def greet(name):
-    return "Hello " + name + "!!"
+#only for dummy data
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+clf = ClassificationModel()
+model_names = clf.get_model_names()
+output_labels = []
+
+def predict(models, img_urls, img_files):
+    print(f'model choosen: {models}')
+    model_predictions = {}
+
+    #set all labels visibility to false
+    for i, name in enumerate(model_names):
+        model_predictions[output_labels[i]] = gr.Label(label=f'# {name}', visible=False)
+        print(f'id {i} invisible')
+    
+    for m in models:
+        idx = model_names.index(m)
+        print(f' {m} idx: ', idx)
+        result = {labels[random.randrange(0, len(labels))]: random.uniform(0, 1.0) for i in range(5)}        
+        model_predictions[output_labels[idx]] =  gr.Label(label=f'# {m}, 3 seconds', value=result, visible=True)
+    
+    return model_predictions
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Image Classification Benchmark")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            model = gr.Dropdown(choices=model_names, multiselect=True, label='Choose the model')
+            img_urls = gr.Textbox(label='Image Urls (separated with comma)')    
+            img_files = gr.File(label='Upload Files',file_count='multiple', file_types=['image'])
+            apply = gr.Button("Classify", variant='primary')
+        with gr.Column(scale=1):
+            for name in clf.get_model_names():
+                output_labels.append(gr.Label(label=f'# {name}'))                       
+
+    apply.click(fn=predict,
+               inputs=[model, img_urls, img_files],
+               outputs=output_labels)
+
+
+if __name__ == "__main__":
+    demo.launch()
+
+# inputs = [
+#     gr.Dropdown(choices=clf.get_model_names(), multiselect=True)
+#     ]
+
+# iface = gr.Interface(fn=greet, inputs=inputs, outputs="text")
+# iface.launch()

src/classification_model.py

@@ -0,0 +1,30 @@
+from .model_data import ModelData
+
+class ClassificationModel:
+    """
+    Base class for all classification models.
+    """
+
+    def __init__(self):
+        self.models = self.initialize_models()
+
+    def get_model_names(self):
+        return [model.name for model in self.models]
+
+    def get_model_data(self, model_name):
+        for model in self.models:
+            if model.name == model_name:
+                return model
+        raise Exception(f'Model {model_name} not found')
+
+    def initialize_models(self):
+        return [
+            ModelData('clip-vit-base-patch32'), 
+            ModelData('mobilenet_v3')
+            ]
+
+    def load_model(self):
+        """
+        Loads the model from the model path.
+        """
+        raise NotImplementedError

src/model_data.py

@@ -0,0 +1,5 @@
+from dataclasses import dataclass
+
+@dataclass
+class ModelData:
+    name: str