test optimization

app.py

@@ -93,7 +93,27 @@ with open('demonstration_3_42_parse.txt', 'r') as f:
 # Your existing code
 theme = gr.themes.Soft()
 
-pipeline = pipeline(task="text-generation", model="lmsys/vicuna-7b-v1.3")
+# Pipeline Initialization
+pipelines = {model: pipeline(task="text2text-generation", model=model_mapping[model]) for model in model_mapping}
+
+# Task-specific Prompts
+task_prompts = {
+    'POS': {
+        'strategy1': 'template_all',
+        'strategy2': 'prompt2_pos',
+        'strategy3': 'demon_pos',
+    },
+    'Chunking': {
+        'strategy1': 'template_all',
+        'strategy2': 'prompt2_chunk',
+        'strategy3': 'demon_chunk',
+    },
+    'Parsing': {
+        'strategy1': 'template_all',
+        'strategy2': 'prompt2_parse',
+        'strategy3': 'demon_parse',
+    },
+}
 
 # Dropdown options for model and task
 model_options = list(model_mapping.keys())
@@ -105,48 +125,13 @@ def process_text(model_name, task, text):
 
     for gid in tqdm(gid_list, desc='Query'):
             text = ptb[gid]['text']
-    
-    #if model_name is 'gpt3.5': 'gpt-3.5-turbo-0613',
-    #elif model_name is 'vicuna-7b': 'lmsys/vicuna-7b-v1.3',
-    #elif model_name is 'vicuna-13b': 'lmsys/vicuna-13b-v1.3',
-    #elif model_name is 'vicuna-33b': 'lmsys/vicuna-33b-v1.3',
-    #elif model_name is 'fastchat-t5': 'lmsys/fastchat-t5-3b-v1.0',
-    #elif model_name is 'llama-7b': './llama/hf/7B',
-    #elif model_name is 'llama-13b': './llama/hf/13B',
-    #elif model_name is 'llama-30b': './llama/hf/30B',
-    #elif model_name is 'alpaca': './alpaca-7B',
-
-    if task == 'POS':
-        strategy1 = pipeline(template_all.format(text))
-        strategy2 = pipeline(prompt2_pos.format(text))
-        strategy3 = pipeline(demon_pos)
-        return (strategy1, strategy2, strategy3)
-    elif task == 'Chunking':
-        strategy1 = pipeline(template_all.format(text))
-        strategy2 = pipeline(prompt2_chunk.format(text))
-        strategy3 = pipeline(demon_chunk)
-        return (strategy1, strategy2, strategy3)
-    elif task == 'Parsing':
-        strategy1 = pipeline(template_all.format(text))
-        strategy2 = pipeline(prompt2_parse.format(text))
-        strategy3 = pipeline(demon_parse)
-        return (strategy1, strategy2, strategy3) 
-    
-    # Define prompts for each strategy based on the task
-    #strategy_prompts = {
-    #    'Strategy 1': template_all.format(text),
-    #    'Strategy 2': {
-    #        'POS': prompt2_pos.format(text),
-    #        'Chunking': prompt2_chunk.format(text),
-    #        'Parsing': prompt2_parse.format(text),
-    #    }.get(task, "Invalid Task Selection for Strategy 2"),
-    #    'Strategy 3': {
-    #        'POS': demon_pos,
-    #        'Chunking': demon_chunk,
-    #        'Parsing': demon_parse,
-    #    }.get(task, "Invalid Task Selection for Strategy 3"),
-    #}
 
+    if model_name in pipelines:
+        strategy1 = task_prompts[task]['strategy1'].format(text)
+        strategy2 = task_prompts[task]['strategy2'].format(text)
+        strategy3 = task_prompts[task]['strategy3'].format(text)
+
+        return tuple(pipelines[model_name](strategy) for strategy in [strategy1, strategy2, strategy3])
 
 # Gradio interface
 iface = gr.Interface(
@@ -161,11 +146,9 @@ iface = gr.Interface(
         gr.Textbox(label="Strategy 2 Instruction Result"),
         gr.Textbox(label="Strategy 3 Structured Prompting Result"),
     ],
-    title = "LLM Evaluator For Linguistic Scrutiny",
-    theme = theme,
+    title="LLM Evaluator For Linguistic Scrutiny",
+    theme=theme,
     live=False,
 )
 
 iface.launch()
-
-