Update app.py

app.py

@@ -11,6 +11,10 @@ from interpret import InterpretationPrompt
 MAX_PROMPT_TOKENS = 30
 
 ## info
+dataset_info = [{'name': 'Commonsense', 'hf_repo': 'tau/commonsense_qa', 'text_col': 'question'}]
+
+
+
 model_info = {
     'LLAMA2-7B': dict(model_path='meta-llama/Llama-2-7b-chat-hf', device_map='cpu', token=os.environ['hf_token'], 
                                           original_prompt_template='<s>[INST] {prompt} [/INST]',
@@ -161,6 +165,8 @@ css = '''
 
 # '''
 
+original_prompt_raw = gr.Textbox(value='Should I eat cake or vegetables?', container=True, label='Original Prompt')
+
 with gr.Blocks(theme=gr.themes.Default(), css=css) as demo:
     global_state = gr.State([])
     with gr.Row():
@@ -190,21 +196,21 @@ with gr.Blocks(theme=gr.themes.Default(), css=css) as demo:
     with gr.Group('Interpretation'):
         interpretation_prompt = gr.Text(suggested_interpretation_prompts[0], label='Interpretation Prompt')
 
-    with gr.Blocks():
+    with gr.Group():
         gr.Markdown('''
         Here are some examples of prompts we can analyze their internal representations
         ''')
-        with gr.Tab('Memory Recall'):
-            pass
-        with gr.Tab('Physics Understanding'):
-            pass
-        with gr.Tab('Common Sense'):
-            pass
-        with gr.Tab('LLM Attacks'):
-            pass
-            
+        
+        for info in dataset_info:
+            with gr.Tab(info['name']):
+                num_examples = 10
+                dataset = load_dataset(info['hf_repo'], split='train', streaming=True)
+                dataset = dataset.shuffle(buffer_size=2000).take(num_examples)
+                dataset = [[row[info['text_col']]] for row in dataset]
+                gr.Examples(dataset, [original_prompt_raw])
+                
     with gr.Group():
-        original_prompt_raw = gr.Textbox(value='Should I eat cake or vegetables?', container=True, label='Original Prompt')
+        original_prompt_raw.render()
         original_prompt_btn = gr.Button('Compute', variant='primary')
 
     tokens_container = []