created function for llama and gpt strategies

app.py

@@ -112,6 +112,31 @@ def llama_respond(tab_name, message, chat_history):
     time.sleep(2)
     return tab_name, "", chat_history
 
+def gpt_strategies_respond(strategy, task_name, task_ling_ent, message, chat_history, max_convo_length = 10):
+    formatted_system_prompt = ""
+    if (task_name == "POS Tagging"):
+        if (strategy == "S1"):
+            formatted_system_prompt = f'''Generate the output only for the assistant. Please output any {task_ling_ent} in the following sentence one per line without any additional text: {message}'''
+        elif (strategy == "S2"):
+            formatted_system_prompt = f'''Please POS tag the following sentence using Universal POS tag set without generating any additional text: {message}'''
+        elif (strategy == "S3"):
+            formatted_system_prompt = f'''Please POS tag the following sentence using Universal POS tag set without generating any additional text: {message}'''
+    elif (task_name == "Chunking"):
+        if (strategy == "S1"):
+            formatted_system_prompt = f'''Generate the output only for the assistant. Please output any {task_ling_ent} in the following sentence one per line without any additional text: {message}'''
+        elif (strategy == "S2"):
+            formatted_system_prompt = f'''Please chunk the following sentence in CoNLL 2000 format with BIO tags without outputing any additional text: {message}'''
+        elif (strategy == "S3"):
+            formatted_system_prompt = f'''Please chunk the following sentence in CoNLL 2000 format with BIO tags without outputing any additional text: {message}'''
+    
+    formatted_prompt = format_chat_prompt(message, chat_history, max_convo_length)
+    print('Prompt + Context:')
+    print(formatted_prompt)
+    bot_message = chat(system_prompt = formatted_system_prompt,
+                       user_prompt = formatted_prompt)
+    chat_history.append((message, bot_message))
+    return "", chat_history
+
 def vicuna_strategies_respond(strategy, task_name, task_ling_ent, message, chat_history):
     formatted_prompt = ""
     if (task_name == "POS Tagging"):
@@ -144,6 +169,38 @@ def vicuna_strategies_respond(strategy, task_name, task_ling_ent, message, chat_
     time.sleep(2)
     return task_name, "", chat_history
 
+def llama_strategies_respond(strategy, task_name, task_ling_ent, message, chat_history):
+    formatted_prompt = ""
+    if (task_name == "POS Tagging"):
+        if (strategy == "S1"):
+            formatted_prompt = f'''Generate the output only for the assistant. Please output any {task_ling_ent} in the following sentence one per line without any additional text: {message}'''
+        elif (strategy == "S2"):
+            formatted_prompt = f'''Please POS tag the following sentence using Universal POS tag set without generating any additional text: {message}'''
+        elif (strategy == "S3"):
+            formatted_prompt = f'''Please POS tag the following sentence using Universal POS tag set without generating any additional text: {message}'''
+    elif (task_name == "Chunking"):
+        if (strategy == "S1"):
+            formatted_prompt = f'''Generate the output only for the assistant. Please output any {task_ling_ent} in the following sentence one per line without any additional text: {message}'''
+        elif (strategy == "S2"):
+            formatted_prompt = f'''Please chunk the following sentence in CoNLL 2000 format with BIO tags without outputing any additional text: {message}'''
+        elif (strategy == "S3"):
+            formatted_prompt = f'''Please chunk the following sentence in CoNLL 2000 format with BIO tags without outputing any additional text: {message}'''
+    
+    # print('Llama Strategies - Prompt + Context:')
+    # print(formatted_prompt)
+    input_ids = llama_tokenizer.encode(formatted_prompt, return_tensors="pt")
+    output_ids = llama_model.generate(input_ids, do_sample=True, max_length=1024, num_beams=5, no_repeat_ngram_size=2)
+    bot_message = llama_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    # print(bot_message)
+    
+    # Remove formatted prompt from bot_message
+    bot_message = bot_message.replace(formatted_prompt, '')
+    # print(bot_message)
+        
+    chat_history.append((formatted_prompt, bot_message))
+    time.sleep(2)
+    return task_name, "", chat_history
+
 def interface():
 
         # prompt = template_single.format(tab_name, textbox_prompt)
@@ -205,11 +262,13 @@ def interface():
                 llama_S1_chatbot = gr.Chatbot(label="llama-7b")
                 gpt_S1_chatbot = gr.Chatbot(label="gpt-3.5")
             gr.Markdown("Strategy 2 Instruction-Based Prompting")
+            strategy2 = gr.Markdown("S2", visible=False)
             with gr.Row():
                 vicuna_S2_chatbot = gr.Chatbot(label="vicuna-7b")
                 llama_S2_chatbot = gr.Chatbot(label="llama-7b")
                 gpt_S2_chatbot = gr.Chatbot(label="gpt-3.5")
             gr.Markdown("Strategy 3 Structured Prompting")
+            strategy3 = gr.Markdown("S3", visible=False)
             with gr.Row():
                 vicuna_S3_chatbot = gr.Chatbot(label="vicuna-7b")
                 llama_S3_chatbot = gr.Chatbot(label="llama-7b")
@@ -223,9 +282,9 @@ def interface():
             # Event Handlers for Vicuna Chatbot POS/Chunk
             task_btn.click(vicuna_strategies_respond, inputs=[strategy1, task, task_linguistic_entities, task_prompt, vicuna_S1_chatbot], 
                       outputs=[task, task_prompt, vicuna_S1_chatbot])
-            task_btn.click(vicuna_strategies_respond, inputs=[strategy1, task, task_linguistic_entities, task_prompt, vicuna_S2_chatbot], 
+            task_btn.click(vicuna_strategies_respond, inputs=[strategy2, task, task_linguistic_entities, task_prompt, vicuna_S2_chatbot], 
                       outputs=[task, task_prompt, vicuna_S2_chatbot])
-            task_btn.click(vicuna_strategies_respond, inputs=[strategy1, task, task_linguistic_entities, task_prompt, vicuna_S3_chatbot], 
+            task_btn.click(vicuna_strategies_respond, inputs=[strategy3, task, task_linguistic_entities, task_prompt, vicuna_S3_chatbot], 
                       outputs=[task, task_prompt, vicuna_S3_chatbot])
             
             # Event Handler for LLaMA Chatbot POS/Chunk