urial chat initial version

app.py

@@ -1,62 +1,143 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+import gradio as gr 
+from openai import OpenAI
+import os
+from typing import List
+import logging
+
+# add logging info to console 
+logging.basicConfig(level=logging.INFO)
+
+
+BASE_URL = "https://api.together.xyz/v1"
+DEFAULT_API_KEY = os.getenv("TOGETHER_API_KEY") 
+import urllib.request
+URIAL_VERSION = "inst_1k_v4.help"
+
+urial_url = f"https://raw.githubusercontent.com/Re-Align/URIAL/main/urial_prompts/{URIAL_VERSION}.txt"
+urial_prompt = urllib.request.urlopen(urial_url).read().decode('utf-8')
+urial_prompt = urial_prompt.replace("```", '"""')
+stop_str = ['"""', '# Query:', '# Answer:']
+
+def urial_template(urial_prompt, history, message):
+    current_prompt = urial_prompt + "\n"
+    for user_msg, ai_msg in history:
+        current_prompt += f'# Query:\n"""\n{user_msg}\n"""\n\n# Answer:\n"""\n{ai_msg}\n"""\n\n'
+    current_prompt += f'# Query:\n"""\n{message}\n"""\n\n# Answer:\n"""\n'
+    return current_prompt
+    
+
+
+
+def openai_base_request(
+    model: str=None, 
+    temperature: float=0,
+    max_tokens: int=512,
+    top_p: float=1.0, 
+    prompt: str=None,
+    n: int=1, 
+    repetition_penalty: float=1.0,
+    stop: List[str]=None, 
+    api_key: str=None,
+    ):  
+    if api_key is None:
+        api_key = DEFAULT_API_KEY
+    client = OpenAI(api_key=api_key, base_url=BASE_URL)
+    # print(f"Requesting chat completion from OpenAI API with model {model}")
+    logging.info(f"Requesting chat completion from OpenAI API with model {model}")
+    logging.info(f"Prompt: {prompt}")
+    logging.info(f"Temperature: {temperature}")
+    logging.info(f"Max tokens: {max_tokens}")
+    logging.info(f"Top-p: {top_p}")
+    logging.info(f"Repetition penalty: {repetition_penalty}")
+    logging.info(f"Stop: {stop}")
+
+    request = client.completions.create(
+        model=model, 
+        prompt=prompt,
+        temperature=float(temperature),
+        max_tokens=int(max_tokens),
+        top_p=float(top_p),
+        n=n,
+        extra_body={'repetition_penalty': float(repetition_penalty)},
+        stop=stop, 
+        stream=True
+    ) 
+    
+    return request 
+
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
 def respond(
     message,
     history: list[tuple[str, str]],
-    system_message,
     max_tokens,
     temperature,
     top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    rp,
+    model_name,
+    together_api_key
+):  
+    global stop_str, urial_prompt
+    rp = 1.0
+    prompt = urial_template(urial_prompt, history, message)
+    if model_name == "Llama-3-8B":
+        _model_name = "meta-llama/Llama-3-8b-hf"
+    elif model_name == "Llama-3-70B":
+        _model_name = "meta-llama/Llama-3-70b-hf"
+    else:
+        raise ValueError("Invalid model name")
+    # _model_name = "meta-llama/Llama-3-8b-hf"
 
-    messages.append({"role": "user", "content": message})
+    if together_api_key and len(together_api_key) == 64:
+        api_key = together_api_key
+    else:
+        api_key = DEFAULT_API_KEY
 
+    request = openai_base_request(prompt=prompt, model=_model_name, 
+                                   temperature=temperature, 
+                                   max_tokens=max_tokens, 
+                                   top_p=top_p, 
+                                   repetition_penalty=rp,
+                                   stop=stop_str, api_key=api_key)  
+    
     response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
+    for msg in request:
+        # print(msg.choices[0].delta.keys())
+        token = msg.choices[0].delta["content"]
         response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+        should_stop = False
+        for _stop in stop_str:
+            if _stop in response:
+                should_stop = True
+                break
+        if should_stop:
+            break
+        yield response 
+ 
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Label("Welcome to the URIAL Chatbot!")
+            model_name = gr.Radio(["Llama-3-8B", "Llama-3-70B"], value="Llama-3-8B", label="Base model name")
+            together_api_key = gr.Textbox(label="Together API Key", placeholder="Enter your Together API Key. Leave it blank if you want to use the default API key.", type="password")
+        with gr.Column():
+            with gr.Column():
+                with gr.Row():
+                    max_tokens = gr.Textbox(value=1024, label="Max tokens")
+                    temperature = gr.Textbox(value=0.5, label="Temperature")
+            with gr.Column():
+                with gr.Row():
+                    top_p = gr.Textbox(value=0.9, label="Top-p")
+                    rp = gr.Textbox(value=1.1, label="Repetition penalty")
+
+    chat = gr.ChatInterface(
+        respond,
+        additional_inputs=[max_tokens, temperature, top_p, rp, model_name, together_api_key],
+        # additional_inputs_accordion="⚙️ Parameters",
+        # fill_height=True, 
+    )
+    chat.chatbot.height = 600
 
 
 if __name__ == "__main__":