Update app.py

app.py

@@ -1,109 +1,26 @@
-import math
-from datasets import load_dataset
-import gradio as gr
 import os
+import gradio as gr
 
-auth_token = os.environ.get("auth_token")
-whoops = load_dataset("nlphuji/whoops", use_auth_token=auth_token)['test']
-# print(f"Loaded WHOOPS!, first example:")
-# print(whoops[0])
-dataset_size = len(whoops)
-
-IMAGE = 'image'
-IMAGE_DESIGNER = 'image_designer'
-DESIGNER_EXPLANATION = 'designer_explanation'
-CROWD_CAPTIONS = 'crowd_captions'
-CROWD_EXPLANATIONS = 'crowd_explanations'
-CROWD_UNDERSPECIFIED_CAPTIONS = 'crowd_underspecified_captions'
-QA = 'question_answering_pairs'
-IMAGE_ID = 'image_id'
-SELECTED_CAPTION = 'selected_caption'
-COMMONSENSE_CATEGORY = 'commonsense_category'
-left_side_columns = [IMAGE]
-right_side_columns = [x for x in whoops.features.keys() if x not in left_side_columns]
-enumerate_cols = [CROWD_CAPTIONS, CROWD_EXPLANATIONS, CROWD_UNDERSPECIFIED_CAPTIONS]
-emoji_to_label = {IMAGE_DESIGNER: '🎨, 🧑‍🎨, 💻', DESIGNER_EXPLANATION: '💡, 🤔, 🧑‍🎨',
-                  CROWD_CAPTIONS: '👥, 💬, 📝', CROWD_EXPLANATIONS: '👥, 💡, 🤔', CROWD_UNDERSPECIFIED_CAPTIONS: '👥, 💬, 👎',
-                  QA: '❓, 🤔, 💡', IMAGE_ID: '🔍, 📄, 💾', COMMONSENSE_CATEGORY: '🤔, 📚, 💡', SELECTED_CAPTION: '📝, 👌, 💬'}
-# batch_size = 16
-batch_size = 8
-target_size = (1024, 1024)
-
-
-def func(index):
-    start_index = index * batch_size
-    end_index = start_index + batch_size
-    all_examples = [whoops[index] for index in list(range(start_index, end_index))]
-    values_lst = []
-    for example_idx, example in enumerate(all_examples):
-        values = get_instance_values(example)
-        values_lst += values
-    return values_lst
-
-
-def get_instance_values(example):
-    values = []
-    for k in left_side_columns + right_side_columns:
-        if k == IMAGE:
-            value = example["image"].resize(target_size)
-        elif k in enumerate_cols:
-            value = list_to_string(example[k])
-        elif k == QA:
-            qa_list = [f"Q: {x[0]} A: {x[1]}" for x in example[k]]
-            value = list_to_string(qa_list)
-        else:
-            value = example[k]
-        values.append(value)
-    return values
-def list_to_string(lst):
-    return '\n'.join(['{}. {}'.format(i+1, item) for i, item in enumerate(lst)])
-
-demo = gr.Blocks()
-
-
-def get_col(example):
-    instance_values = get_instance_values(example)
-    with gr.Column():
-        inputs_left = []
-        assert len(left_side_columns) == len(
-            instance_values[:len(left_side_columns)])  # excluding the image & designer
-        for key, value in zip(left_side_columns, instance_values[:len(left_side_columns)]):
-            if key == IMAGE:
-                img_resized = example["image"].resize(target_size)
-                # input_k = gr.Image(value=img_resized, label=example['commonsense_category'])
-                input_k = gr.Image(value=img_resized)
-            else:
-                label = key.capitalize().replace("_", " ")
-                input_k = gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}")
-            inputs_left.append(input_k)
-        with gr.Accordion("Click for details", open=False):
-            text_inputs_right = []
-            assert len(right_side_columns) == len(
-                instance_values[len(left_side_columns):])  # excluding the image & designer
-            for key, value in zip(right_side_columns, instance_values[len(left_side_columns):]):
-                label = key.capitalize().replace("_", " ")
-                text_input_k = gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}")
-                text_inputs_right.append(text_input_k)
-    return inputs_left, text_inputs_right
+# Path to the directory containing HTML files
+html_files_path = "html_files"
+html_files = [f for f in os.listdir(html_files_path) if f.endswith('.html')]
 
+def show_html(file_name):
+    with open(os.path.join(html_files_path, file_name), 'r', encoding='utf-8') as file:
+        return file.read()
 
-with demo:
-    gr.Markdown("# Slide to iterate WHOOPS!")
+# Create a dropdown to select an HTML file
+file_dropdown = gr.Dropdown(label="Select an HTML file", choices=html_files, value=html_files[0])
 
-    with gr.Column():
-        num_batches = math.ceil(dataset_size / batch_size)
-        slider = gr.Slider(minimum=0, maximum=num_batches, step=1, label=f'Page (out of {num_batches})')
-        with gr.Row():
-            index = slider.value
-            start_index = 0 * batch_size
-            end_index = start_index + batch_size
-            all_examples = [whoops[index] for index in list(range(start_index, end_index))]
-            all_inputs_left_right = []
-            for example_idx, example in enumerate(all_examples):
-                inputs_left, text_inputs_right = get_col(example)
-                inputs_left_right = inputs_left + text_inputs_right
-                all_inputs_left_right += inputs_left_right
+# Create an HTML component to display the selected HTML file's content
+html_output = gr.HTML()
 
-    slider.change(func, inputs=[slider], outputs=all_inputs_left_right)
+# Interface
+iface = gr.Interface(fn=show_html, 
+                     inputs=file_dropdown, 
+                     outputs=html_output, 
+                     title="HTML File Viewer",
+                     description="Select an HTML file to view its content.")
 
-demo.launch()
+if __name__ == "__main__":
+    iface.launch()