test

app.py

@@ -1,7 +1,16 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import time
+import os
 import openai
+import json
+import re
+import io
+import IPython.display
+from PIL import Image
+import base64 
+import requests, json
+requests.adapters.DEFAULT_TIMEOUT = 60
 
 # Load the Vicuna 7B v1.3 LMSys model and tokenizer
 model_name = "lmsys/vicuna-7b-v1.3"
@@ -10,10 +19,45 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 
 template_single = '''Please output any <{}> in the following sentence one per line without any additional text: "{}"'''
 
-def gpt3(prompt):
+#API Keys
+os.environ['OPENAI_API_TOKEN'] = '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'
+openai.api_key = os.environ['OPENAI_API_TOKEN']
+
+def chat(system_prompt, user_prompt, model = 'gpt-3.5-turbo', temperature = 0, verbose = False):
+    ''' Normal call of OpenAI API '''
     response = openai.ChatCompletion.create(
-        model='gpt3.5', messages=[{"role": "user", "content": prompt}])
-    return response['choices'][0]['message']['content']
+    temperature = temperature,
+    model=model,
+    messages=[
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt}
+    ])
+    
+    res = response['choices'][0]['message']['content']
+    
+    if verbose:
+        print('System prompt:', system_prompt)
+        print('User prompt:', user_prompt)
+        print('GPT response:', res)
+        
+    return res
+
+def format_chat_prompt(message, chat_history, max_convo_length):
+    prompt = ""
+    for turn in chat_history[-max_convo_length:]:
+        user_message, bot_message = turn
+        prompt = f"{prompt}\nUser: {user_message}\nAssistant: {bot_message}"
+    prompt = f"{prompt}\nUser: {message}\nAssistant:"
+    return prompt
+
+def respond_gpt(tab_name, message, chat_history, max_convo_length = 10):
+        formatted_prompt = format_chat_prompt(message, chat_history, max_convo_length)
+        print('Prompt + Context:')
+        print(formatted_prompt)
+        bot_message = chat(system_prompt = f'''Generate the output only for the assistant. Please output any <{tab_name}> in the following sentence one per line without any additional text.''',
+                           user_prompt = formatted_prompt)
+        chat_history.append((message, bot_message))
+        return "", chat_history
 
 def respond(message, chat_history):
     input_ids = tokenizer.encode(message, return_tensors="pt")
@@ -24,15 +68,12 @@ def respond(message, chat_history):
     time.sleep(2)
     return "", chat_history
 
-def textbox_submit(tab_name, textbox, chatbot):
-    prompt = template_single.format(tab_name, textbox.value)
-    respond(prompt, chatbot)
-
 def interface(tab_name):
         gr.Markdown(" Description ")
 
         textbox_prompt = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
         api_key = gr.Textbox(label="Open AI Key", placeholder="Enter your Openai key here", type="password")
+        btn = gr.Button("Submit")
 
         prompt = template_single.format(tab_name, textbox_prompt)
 
@@ -41,23 +82,25 @@ def interface(tab_name):
             vicuna_S1_chatbot = gr.Chatbot(label="vicuna-7b")
             llama_S1_chatbot = gr.Chatbot(label="llama-7b")
             gpt_S1_chatbot = gr.Chatbot(label="gpt-3.5")
-        clear = gr.ClearButton([textbox_prompt, vicuna_S1_chatbot])
+        clear = gr.ClearButton(components=[textbox_prompt, vicuna_S1_chatbot])
         gr.Markdown("Strategy 2 Instruction-Based Prompting")
         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")
-        clear = gr.ClearButton([textbox_prompt, vicuna_S2_chatbot])
+        clear = gr.ClearButton(components=[textbox_prompt, vicuna_S2_chatbot])
         gr.Markdown("Strategy 3 Structured Prompting")
         with gr.Row():
             vicuna_S3_chatbot = gr.Chatbot(label="vicuna-7b")
             llama_S3_chatbot = gr.Chatbot(label="llama-7b")
             gpt_S3_chatbot = gr.Chatbot(label="gpt-3.5")
-        clear = gr.ClearButton([textbox_prompt, vicuna_S3_chatbot])
+        clear = gr.ClearButton(components=[textbox_prompt, vicuna_S3_chatbot])
+
+        textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S1_chatbot], outputs=[textbox_prompt, vicuna_S1_chatbot])
+        textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S2_chatbot], outputs=[textbox_prompt, vicuna_S2_chatbot])
+        textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S3_chatbot], outputs=[textbox_prompt, vicuna_S3_chatbot])
 
-        textbox_prompt.submit(textbox_submit, tab_name, textbox_prompt, vicuna_S1_chatbot)
-        textbox_prompt.submit(textbox_submit, tab_name, textbox_prompt, vicuna_S2_chatbot)
-        textbox_prompt.submit(textbox_submit, tab_name, textbox_prompt, vicuna_S3_chatbot)
+        btn.click(respond_gpt, inputs=[tab_name, textbox_prompt, gpt_S1_chatbot], outputs=[tab_name, textbox_prompt, gpt_S1_chatbot])
 
 with gr.Blocks() as demo:
     gr.Markdown("# LLM Evaluator With Linguistic Scrutiny")