Update app.py

app.py

@@ -1,75 +1,114 @@
-from pyChatGPT import ChatGPT
 import gradio as gr
-import os, json
-from loguru import logger
-import random
-from transformers import pipeline
-import torch
+import os 
+import json 
+import requests
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2")
-openai_chatgpt = gr.Interface.load(name="spaces/anzorq/chatgpt-demo")
 
 #input_message.submit([input_message, history], [input_message, chatbot, history])
 def translate_or_transcribe(audio, task):
     text_result = whisper(audio, None, task, fn_index=0)
     return text_result
     
-def get_response_from_chatbot(text, chat_history):
-    r = openai_chatgpt(message, chat_history)
-    response = "Sorry, the chatGPT queue is full. Please try again in some time"
-    return response
 
-def chat(message, chat_history):      
-    out_chat = []
-    if chat_history != '':
-        out_chat = json.loads(chat_history)
-    response = get_response_from_chatbot(message, chat_history)
-    out_chat.append((message, response))
-    chat_history = json.dumps(out_chat)
-    logger.info(f"out_chat_: {len(out_chat)}")
-    return out_chat, chat_history
 
+#Streaming endpoint
+API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
 
 
-with gr.Blocks(title='Talk to chatGPT') as demo:
-    gr.Markdown("## Talk to chatGPT with your voice in your native language ! ##")
+def predict(inputs, top_p, temperature, openai_api_key, history=[]):  
+
+    payload = {
+    "model": "gpt-3.5-turbo",
+    "messages": [{"role": "user", "content": f"{inputs}"}],
+    "temperature" : 1.0,
+    "top_p":1.0,
+    "n" : 1,
+    "stream": True,
+    "presence_penalty":0,
+    "frequency_penalty":0,
+    }
+
+    headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {openai_api_key}"
+    }
+
     
-    with gr.Group(elem_id="page_2") as page_2:        
-        with gr.Row(elem_id="chat_row"):
-            chatbot = gr.Chatbot(elem_id="chat_bot").style(color_map=("green", "blue"))    
-        with gr.Row():
-            prompt_input_audio = gr.Audio(
-                                        source="microphone",
-                                        type="filepath",
-                                        label="Record Audio Input",
-                                        
-                                    )
-            translate_btn = gr.Button("Check Whisper first ? 👍")
-            
-        whisper_task = gr.Radio(["translate", "transcribe"], value="transcribe", show_label=False)
-        with gr.Row(elem_id="prompt_row"):
-            prompt_input = gr.Textbox(lines=2, label="Input text",show_label=True)
-            chat_history = gr.Textbox(lines=4, label="prompt", visible=False)
-            submit_btn = gr.Button(value = "Send to chatGPT",elem_id="submit-btn").style(
-                    margin=True,
-                    rounded=(True, True, True, True),
-                    width=100
-                )
-            
-             
+    history.append(inputs)
+    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
+    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
+    #response = requests.post(API_URL, headers=headers, json=payload, stream=True)
+    token_counter = 0 
+    partial_words = "" 
 
-        translate_btn.click(fn=translate_or_transcribe, 
-                         inputs=[prompt_input_audio,whisper_task], 
-                         outputs=prompt_input
-                        )
+    counter=0
+    for chunk in response.iter_lines():
+        if counter == 0:
+          counter+=1
+          continue
+        counter+=1
+        # check whether each line is non-empty
+        if chunk :
+          # decode each line as response data is in bytes
+          if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
+            break
+          #print(json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"])
+          partial_words = partial_words + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
+          if token_counter == 0:
+            history.append(" " + partial_words)
+          else:
+            history[-1] = partial_words
+          chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
+          token_counter+=1
+          yield chat, history # resembles {chatbot: chat, state: history}  
         
-          
-        submit_btn.click(fn=chat, 
-                             inputs=[prompt_input, chat_history], 
-                             outputs=[chatbot, chat_history],
-                            )
+
+def reset_textbox():
+    return gr.update(value='')
+
+title = """<h1 align="center">🔥ChatGPT API 🚀Streaming🚀</h1>"""
+description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
+```
+User: <utterance>
+Assistant: <utterance>
+User: <utterance>
+Assistant: <utterance>
+...
+```
+In this app, you can explore the outputs of a 20B large language model.
+"""
+#<a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate Space with GPU Upgrade for fast Inference & no queue<br> 
+                
+with gr.Blocks(css = """#col_container {width: 700px; margin-left: auto; margin-right: auto;}
+                #chatbot {height: 400px; overflow: auto;}""") as demo:
+    gr.HTML(title)
+    gr.HTML()
+    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
+    with gr.Column(elem_id = "col_container"):
+        openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
+        chatbot = gr.Chatbot(elem_id='chatbot') #c
+        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
+        state = gr.State([]) #s
+        b1 = gr.Button()
+    
+        #inputs, top_p, temperature, top_k, repetition_penalty
+        with gr.Accordion("Parameters", open=False):
+            top_p = gr.Slider( minimum=-0, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
+            temperature = gr.Slider( minimum=-0, maximum=5.0, value=0.5, step=0.1, interactive=True, label="Temperature",)
+            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
+            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
+    
+
+    inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, state], [chatbot, state],)
+    b1.click( predict, [inputs, top_p, temperature, openai_api_key, state], [chatbot, state],)
+    b1.click(reset_textbox, [], [inputs])
+    inputs.submit(reset_textbox, [], [inputs])
+                    
+    #gr.Markdown(description)
+                    
     gr.HTML('''
          <p>Note: Please be aware that audio records from iOS devices will not be decoded as expected by Gradio. For the best experience, record your voice from a computer instead of your smartphone ;)</p>
         <div class="footer">
@@ -80,4 +119,4 @@ with gr.Blocks(title='Talk to chatGPT') as demo:
         ''')
     gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=RamAnanth1.chatGPT_voice)")
 
-demo.launch(debug = True)
+    demo.queue().launch(debug=True)