Upload Images

app.py

@@ -1,12 +1,48 @@
-from typing import List
+import io
+import requests
 import gradio as gr
 # from transformers import AutoModel, AutoTokenizer
 from huggingface_hub import list_models
+from datasets import load_dataset
+from typing import List
+from PIL import Image
+
+
+def get_image_names(dataset):
+    return [str(i) for i in range(len(dataset))]
+
+def get_image_from_dataset(index):
+    image_data = dataset[int(index)]["image"]
+    return image_data
+
+def process_image(image=None, dataset_image_index=None):
+    if dataset_image_index:
+        image = get_image_from_dataset(dataset_image_index)
+
+    return image
+
+
+
+def create_interface(tag, image_indices):
+    """ Create Gradio interface"""
+    iface = gr.Interface(
+        fn=process_image,
+        inputs=[
+            gr.Dropdown(choices=get_collection_models(tag), label="Select Model"),
+            gr.Image(type="pil", label="Upload Image"),
+            gr.Dropdown(choices=image_indices, label="Select one from MERIT Dataset test-set"),
+        ],
+        outputs=gr.Image(label="Output Image"),
+        title="Saliency Visualization",
+        description="Upload your image or select one from the MERIT Dataset test-set."
+    )
+    return iface
+
 
 def get_collection_models(tag: str) -> List[str]:
     """Get a list of models from a specific Hugging Face collection."""
     models = list_models(author="de-Rodrigo")
-    
+
     model_names = []
     for model in models:
         if tag in model.tags:
@@ -20,26 +56,19 @@ def load_model(model_name: str):
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     return model, tokenizer
 
-# Example processing function
-def process_input(text: str, model_name: str) -> str:
-    model, tokenizer = load_model(model_name)
-    inputs = tokenizer(text, return_tensors="pt")
-    outputs = model(**inputs)
-    return f"Processed output with {model_name}"
+# # Example processing function
+# def process_input(text: str, model_name: str) -> str:
+#     model, tokenizer = load_model(model_name)
+#     inputs = tokenizer(text, return_tensors="pt")
+#     outputs = model(**inputs)
+#     return f"Processed output with {model_name}"
 
-# Create Gradio interface
-def create_interface(tag: str):
-    iface = gr.Interface(
-        fn=process_input,
-        inputs=[
-            gr.Textbox(lines=2, placeholder="Enter some text", label="Input Text"),
-            gr.Dropdown(choices=get_collection_models(tag), label="Select Model")
-        ],
-        outputs=gr.Textbox(label="Model Output"),
-        title="Hugging Face Model Selector from Collection")
-    return iface
 
-# Specify the name of your collection
-filter_tag = "saliency-merit"
-iface = create_interface(filter_tag)
+dataset_name = "de-Rodrigo/merit"
+dataset = load_dataset(dataset_name, name="en-digital-seq", split="train", num_proc=8)
+image_indices = get_image_names(dataset)
+
+models_tag = "saliency-merit"
+
+iface = create_interface(models_tag, image_indices)
 iface.launch()

requirements.txt

@@ -4,3 +4,4 @@ huggingface_hub
 torch
 numpy
 Pillow
+datasets