test

app.py

@@ -50,15 +50,9 @@ for i, j in zip(ents, ents_prompt):
     print(i, j)
 
 model_mapping = {
-    #'gpt3.5': 'gpt2',
+    'gpt3.5': 'gpt-3.5-turbo-0613',
     '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',
+    'llama-7b': './llama/hf/7B',
 }
 
 with open('sample_uniform_1k_2.txt', 'r') as f:
@@ -93,20 +87,14 @@ with open('demonstration_3_42_parse.txt', 'r') as f:
 # Your existing code
 theme = gr.themes.Soft()
 
-
-#gpt_pipeline = pipeline(task="text2text-generation", model="gpt2")
+# issue get request for gpt 3.5
+gpt_pipeline = pipeline(task="text2text-generation", model="gpt3.5")
 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")
+llama7b_pipeline = pipeline(task="text2text-generation", model="./llama/hf/7B")
 
 # Dropdown options for model and task
 model_options = list(model_mapping.keys())
-task_options = ['POS', 'Chunking', 'Parsing']
+task_options = ['POS', 'Chunking'] # remove parsing
 
 
 # Function to process text based on model and task
@@ -122,27 +110,18 @@ def process_text(model_name, task, text):
             strategy2_format = prompt2_pos.format(text)
             strategy3_format = demon_pos
 
-            result1 = vicuna7b_pipeline(strategy1_format)[0]['generated_text']
-            result2 = vicuna7b_pipeline(strategy2_format)[0]['generated_text']
-            result3 = vicuna7b_pipeline(strategy3_format)[0]['generated_text']
+            result1 = gpt_pipeline(strategy1_format)[0]['generated_text']
+            result2 = gpt_pipeline(strategy2_format)[0]['generated_text']
+            result3 = gpt_pipeline(strategy3_format)[0]['generated_text']
             return (result1, result2, result3)
         elif task == 'Chunking':
             strategy1_format = template_all.format(text)
             strategy2_format = prompt2_chunk.format(text)
             strategy3_format = demon_chunk
 
-            result1 = vicuna7b_pipeline(strategy1_format)[0]['generated_text']
-            result2 = vicuna7b_pipeline(strategy2_format)[0]['generated_text']
-            result3 = vicuna7b_pipeline(strategy3_format)[0]['generated_text']
-            return (result1, result2, result3)
-        elif task == 'Parsing':
-            strategy1_format = template_all.format(text)
-            strategy2_format = prompt2_parse.format(text)
-            strategy3_format = demon_parse
-            
-            result1 = vicuna7b_pipeline(strategy1_format)[0]['generated_text']
-            result2 = vicuna7b_pipeline(strategy2_format)[0]['generated_text']
-            result3 = vicuna7b_pipeline(strategy3_format)[0]['generated_text']
+            result1 = gpt_pipeline(strategy1_format)[0]['generated_text']
+            result2 = gpt_pipeline(strategy2_format)[0]['generated_text']
+            result3 = gpt_pipeline(strategy3_format)[0]['generated_text']
             return (result1, result2, result3)
 
 # Gradio interface