Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 
 import os 
 import time 
@@ -7,11 +6,6 @@ import asyncio
 
 from pipeline import PromptEnhancer
 
-"""
-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")
-
 
 async def advancedPromptPipeline(InputPrompt, model="gpt-4o-mini", temperature=0.0):
     
@@ -29,74 +23,27 @@ async def advancedPromptPipeline(InputPrompt, model="gpt-4o-mini", temperature=0
     elapsed_time = time.time() - start_time
 
 
-    """return {
-        "model": model,
+    return {
+        #"model": model,
         "elapsed_time": elapsed_time,
-        "prompt_tokens": enhancer.prompt_tokens,
-        "completion_tokens": enhancer.completion_tokens,
+        #"prompt_tokens": enhancer.prompt_tokens,
+        #"completion_tokens": enhancer.completion_tokens,
         "approximate_cost": (enhancer.prompt_tokens*i_cost)+(enhancer.completion_tokens*o_cost),
-        "inout_prompt": input_prompt,
+        #"inout_prompt": input_prompt,
         "advanced_prompt": advanced_prompt["advanced_prompt"],
-    }"""
-
-    return advanced_prompt["advanced_prompt"]
-
-
-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]})
-    #
-    #messages.append({"role": "user", "content": message})
-    
-    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
+    }
 
-    #    response += token
-    #    yield response
+    #return advanced_prompt["advanced_prompt"]
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-#demo = gr.ChatInterface(
-    #advancedPromptPipeline,
-#    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)",
-    #    ),
-    #],
-#)
 
 demo = gr.Interface(fn=advancedPromptPipeline, 
                     inputs=[
-                        gr.Textbox(lines=10, placeholder="Enter your prompt", label="Input Prompt", min_width=100),
+                        gr.Textbox(lines=14, placeholder="Enter your prompt", label="Input Prompt", min_width=100),
                         gr.Radio(["gpt-4o-mini", "gpt-4o"], value="gpt-4o-mini", label="Select Model"),
                         gr.Slider(minimum=0.0, maximum=1.0, value=0.0, step=0.1, label="Temperature")
                         ], 
                     outputs=[
-                        gr.Textbox(lines=20, label="Advanced Prompt", show_copy_button=True, autoscroll=False, min_width=220),
+                        gr.Textbox(lines=25, label="Advanced Prompt", show_copy_button=True, autoscroll=False, min_width=220),
                         ]
                    )