test

app.py

@@ -51,6 +51,14 @@ for i, j in zip(ents, ents_prompt):
 
 model_mapping = {
     'gpt3.5': 'gpt2',
+    #'vicuna-7b': 'lmsys/vicuna-7b-v1.3',
+    #'vicuna-13b': 'lmsys/vicuna-13b-v1.3',
+    #'vicuna-33b': 'lmsys/vicuna-33b-v1.3',
+    #'fastchat-t5': 'lmsys/fastchat-t5-3b-v1.0',
+    #'llama-7b': './llama/hf/7B',
+    #'llama-13b': './llama/hf/13B',
+    #'llama-30b': './llama/hf/30B',
+    #'alpaca': './alpaca-7B',
 }
 
 with open('sample_uniform_1k_2.txt', 'r') as f:
@@ -85,50 +93,62 @@ with open('demonstration_3_42_parse.txt', 'r') as f:
 # Your existing code
 theme = gr.themes.Soft()
 
-# 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',
-    },
-}
+
+gpt_pipeline = pipeline(task="text2text-generation", model="gpt2")
+#vicuna7b_pipeline = pipeline(task="text2text-generation", model="lmsys/vicuna-7b-v1.3")
+#vicuna13b_pipeline = pipeline(task="text2text-generation", model="lmsys/vicuna-13b-v1.3")
+#vicuna33b_pipeline = pipeline(task="text2text-generation", model="lmsys/vicuna-33b-v1.3")
+#fastchatT5_pipeline = pipeline(task="text2text-generation", model="lmsys/fastchat-t5-3b-v1.0")
+#llama7b_pipeline = pipeline(task="text2text-generation", model="./llama/hf/7B")
+#llama13b_pipeline = pipeline(task="text2text-generation", model="./llama/hf/13B")
+#llama30b_pipeline = pipeline(task="text2text-generation", model="./llama/hf/30B")
+#alpaca_pipeline = pipeline(task="text2text-generation", model="./alpaca-7B")
 
 # Dropdown options for model and task
 model_options = list(model_mapping.keys())
 task_options = ['POS', 'Chunking', 'Parsing']
 
+
+
 # Function to process text based on model and task
 def process_text(model_name, task, text):
     gid_list = selected_idx[0:20]
 
     for gid in tqdm(gid_list, desc='Query'):
             text = ptb[gid]['text']
+    
+    if model_name is 'gpt3.5': 
+        if task == 'POS':
+            strategy1 = template_all.format(text)
+            strategy2 = prompt2_pos.format(text)
+            strategy3 = demon_pos
+            return (gpt_pipeline(strategy1), gpt_pipeline(strategy2), gpt_pipeline(strategy3))
+        elif task == 'Chunking':
+            strategy1 = template_all.format(text)
+            strategy2 = prompt2_chunk.format(text)
+            strategy3 = demon_chunk
+            return (gpt_pipeline(strategy1), gpt_pipeline(strategy2), gpt_pipeline(strategy3))
+        elif task == 'Parsing':
+            strategy1 = template_all.format(text)
+            strategy2 = prompt2_parse.format(text)
+            strategy3 = demon_parse
+            return (gpt_pipeline(strategy1), gpt_pipeline(strategy2), gpt_pipeline(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)
-
-        # Extract generated text from the model's response
-        response1 = pipelines[model_name](strategy1)[0]['generated_text']
-        response2 = pipelines[model_name](strategy2)[0]['generated_text']
-        response3 = pipelines[model_name](strategy3)[0]['generated_text']
-
-        return response1, response2, response3
 
 # Gradio interface
 iface = gr.Interface(
@@ -143,9 +163,11 @@ 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()
+
+