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README.md

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+---
+title: ChaTOR
+emoji: 💬
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.21.0
+app_file: app.py
+pinned: true
+license: mit
+duplicated_from:
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+import sys
+import json 
+import requests
+
+MODEL = "gpt-3.5-turbo"
+API_URL = os.getenv("API_URL")
+DISABLED = os.getenv("DISABLED") == 'True'
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+NUM_THREADS = int(os.getenv("NUM_THREADS"))
+
+print (NUM_THREADS)
+
+def exception_handler(exception_type, exception, traceback):
+    print("%s: %s" % (exception_type.__name__, exception))
+sys.excepthook = exception_handler
+sys.tracebacklimit = 0
+
+#https://github.com/gradio-app/gradio/issues/3531#issuecomment-1484029099
+def parse_codeblock(text):
+    lines = text.split("\n")
+    for i, line in enumerate(lines):
+        if "```" in line:
+            if line != "```":
+                lines[i] = f'<pre><code class="{lines[i][3:]}">'
+            else:
+                lines[i] = '</code></pre>'
+        else:
+            if i > 0:
+                lines[i] = "<br/>" + line.replace("<", "&lt;").replace(">", "&gt;")
+    return "".join(lines)
+    
+def predict(inputs, top_p, temperature, chat_counter, chatbot, history, request:gr.Request):
+    payload = {
+        "model": MODEL,
+        "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}",
+        "Headers": f"{request.kwargs['headers']}"
+    }
+
+    # print(f"chat_counter - {chat_counter}")
+    if chat_counter != 0 :
+        messages = []
+        for i, data in enumerate(history):
+            if i % 2 == 0:
+                role = 'user'
+            else:
+                role = 'assistant'
+            message = {}
+            message["role"] = role
+            message["content"] = data
+            messages.append(message)
+        
+        message = {}
+        message["role"] = "user" 
+        message["content"] = inputs
+        messages.append(message)
+        payload = {
+            "model": MODEL,
+            "messages": messages,
+            "temperature" : temperature,
+            "top_p": top_p,
+            "n" : 1,
+            "stream": True,
+            "presence_penalty":0,
+            "frequency_penalty":0,
+        }
+
+    chat_counter += 1
+
+    history.append(inputs)
+    token_counter = 0 
+    partial_words = "" 
+    counter = 0
+
+    try:
+        # 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_code = f"{response}"
+        #if response_code.strip() != "<Response [200]>":
+        #    #print(f"response code - {response}")
+        #    raise Exception(f"Sorry, hitting rate limit. Please try again later. {response}")
+        
+        for chunk in response.iter_lines():
+            #Skipping first chunk
+            if counter == 0:
+                counter += 1
+                continue
+                #counter+=1
+            # check whether each line is non-empty
+            if chunk.decode() :
+                chunk = chunk.decode()
+                # decode each line as response data is in bytes
+                if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
+                    partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
+                    if token_counter == 0:
+                        history.append(" " + partial_words)
+                    else:
+                        history[-1] = partial_words
+                    token_counter += 1
+                    yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=False), gr.update(interactive=False)  # resembles {chatbot: chat, state: history}  
+    except Exception as e:
+        print (f'error found: {e}')
+    yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=True), gr.update(interactive=True)
+    print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter}))
+                   
+
+def reset_textbox():
+    return gr.update(value='', interactive=False), gr.update(interactive=False)
+
+title = """<h1 align="center">GPT-3.5 Chatbot</h1>"""
+if DISABLED:
+    title = """<h1 align="center" style="color:red">This app has reached OpenAI's usage limit. We are currently requesting an increase in our quota. Please check back in a few days.</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 gpt-3.5 LLM.
+"""
+
+theme = gr.themes.Default(primary_hue="green")                
+
+with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
+                #chatbot {height: 520px; overflow: auto;}""",
+              theme=theme) as demo:
+    gr.HTML(title)
+    gr.HTML("""<h3 align="center">ChaTOR: В данный момент наши сервера активны, пользуйтесь!</h1>""")
+    #gr.HTML('''<center><a href="https://huggingface.co/spaces/yuntian-deng/ChatGPT?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", visible=False) as main_block:
+        #API Key is provided by OpenAI 
+        #openai_api_key = gr.Textbox(type='password', label="Enter only your OpenAI API key here")
+        chatbot = gr.Chatbot(elem_id='chatbot') #c
+        inputs = gr.Textbox(placeholder= "Привет, я ChaTOr!", label= "Введите запрос и нажмите Enter") #t
+        state = gr.State([]) #s
+        with gr.Row():
+            with gr.Column(scale=7):
+                b1 = gr.Button(visible=not DISABLED).style(full_width=True)
+            with gr.Column(scale=3):
+                server_status_code = gr.Textbox(label="СТАТУС НАШИХ СЕРВЕРОВ", )
+    
+        #inputs, top_p, temperature, top_k, repetition_penalty
+        with gr.Accordion("Настройки", open=False):
+            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (проба ядра)",)
+            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Поведение",)
+            #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", )
+            chat_counter = gr.Number(value=0, visible=False, precision=0)
+    
+    with gr.Column(elem_id = "user_consent_container") as user_consent_block:
+        # Get user consent
+        accept_checkbox = gr.Checkbox(visible=False)
+        js = "(x) => confirm('Когда вы нажмёте \"OK\", чат запуститься')"
+        with gr.Accordion("User Consent for Data Collection, Use, and Sharing", open=True):
+            gr.HTML("""
+            Сделано Евгением Христофору!
+            """)
+            accept_button = gr.Button("Понятно")
+
+        def enable_inputs():
+            return user_consent_block.update(visible=False), main_block.update(visible=True)
+
+    accept_button.click(None, None, accept_checkbox, _js=js, queue=False)
+    accept_checkbox.change(fn=enable_inputs, inputs=[], outputs=[user_consent_block, main_block], queue=False)
+
+    inputs.submit(reset_textbox, [], [inputs, b1], queue=False)
+    inputs.submit(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],)  #openai_api_key
+    b1.click(reset_textbox, [], [inputs, b1], queue=False)
+    b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],)  #openai_api_key
+             
+    demo.queue(max_size=20, concurrency_count=NUM_THREADS, api_open=False).launch(share=False)

gitattributes.txt

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