Update app.py

app.py

@@ -1,65 +1,160 @@
 import gradio as gr
-import openai  # OpenAI 라이브러리 사용
-import os  # 환경 변수를 사용하기 위해 추가
+import os 
+import json 
+import requests
 
-# API 키를 환경 변수에서 가져오기
-api_key = "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"
+#Streaming endpoint 
+API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
 
-# OpenAI API 초기화
-openai.api_key = api_key
+#Inferenec function
+def predict(openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
 
-# 텍스트 분류 함수
-def classify_and_generate_response(text):
-    # 입력된 텍스트를 요약합니다. (텍스트 요약 로직을 추가하세요)
-    summarized_text = "텍스트를 요약한 내용입니다."  # 요약 로직을 추가하세요.
+    headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {openai_gpt4_key}"  #Users will provide their own OPENAI_API_KEY 
+    }
+    print(f"system message is ^^ {system_msg}")
+    if system_msg.strip() == '':
+        initial_message = [{"role": "user", "content": f"{inputs}"},]
+        multi_turn_message = []
+    else:
+        initial_message= [{"role": "system", "content": system_msg},
+                   {"role": "user", "content": f"{inputs}"},]
+        multi_turn_message = [{"role": "system", "content": system_msg},]
+        
+    if chat_counter == 0 :
+        payload = {
+        "model": "gpt-4",
+        "messages": initial_message , 
+        "temperature" : 1.0,
+        "top_p":1.0,
+        "n" : 1,
+        "stream": True,
+        "presence_penalty":0,
+        "frequency_penalty":0,
+        }
+        print(f"chat_counter - {chat_counter}")
+    else: #if chat_counter != 0 :
+        messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
+        for data in chatbot:
+          user = {}
+          user["role"] = "user" 
+          user["content"] = data[0] 
+          assistant = {}
+          assistant["role"] = "assistant" 
+          assistant["content"] = data[1]
+          messages.append(user)
+          messages.append(assistant)
+        temp = {}
+        temp["role"] = "user" 
+        temp["content"] = inputs
+        messages.append(temp)
+        #messages
+        payload = {
+        "model": "gpt-4",
+        "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
+        "temperature" : temperature, #1.0,
+        "top_p": top_p, #1.0,
+        "n" : 1,
+        "stream": True,
+        "presence_penalty":0,
+        "frequency_penalty":0,}
 
-    # 텍스트를 분류합니다.
-    category = classify_text(text)
+    chat_counter+=1
 
-    # ChatGPT를 사용하여 대응 방안을 생성합니다.
-    response = generate_response(f"대응 방안: {category}")
+    history.append(inputs)
+    print(f"Logging : payload is - {payload}")
+    # 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)
+    print(f"Logging : response code - {response}")
+    token_counter = 0 
+    partial_words = "" 
 
-    return category, summarized_text, response
+    counter=0
+    for chunk in response.iter_lines():
+        #Skipping first chunk
+        if counter == 0:
+          counter+=1
+          continue
+        # 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
+              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, chat_counter, response  # resembles {chatbot: chat, state: history}  
+                   
+#Resetting to blank
+def reset_textbox():
+    return gr.update(value='')
 
-# 텍스트를 분류하는 함수
-def classify_text(text):
-    keywords = {
-        "교통": ["차량", "교통사고"],
-        "시위": ["압사", "도로 막힘", "테러"],
-        "날씨": ["날씨로 인한 사고", "수도관 동파", "산불"],
-        "재난": ["지진", "해일"],
-        "주의": ["기타 안전사고"]
-    }
+#to set a component as visible=False
+def set_visible_false():
+    return gr.update(visible=False)
+
+#to set a component as visible=True
+def set_visible_true():
+    return gr.update(visible=True)
+
+title = """<h1 align="center">🔥GPT4 using Chat-Completions API & 🚀Gradio-Streaming</h1>"""
+#display message for themes feature
+theme_addon_msg = """<center>🌟 This Demo also introduces you to Gradio Themes. Discover more on Gradio website using our <a href="https://gradio.app/theming-guide/" target="_blank">Themeing-Guide🎨</a>! You can develop from scratch, modify an existing Gradio theme, and share your themes with community by uploading them to huggingface-hub easily using <code>theme.push_to_hub()</code>.</center>
+""" 
 
-    for category, category_keywords in keywords.items():
-        for keyword in category_keywords:
-            if keyword in text:
-                return category
+#Using info to add additional information about System message in GPT4
+system_msg_info = """A conversation could begin with a system message to gently instruct the assistant. 
+System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
 
-    return "주의"  # 어떤 카테고리에도 속하지 않을 경우 "주의"로 분류
+#Modifying existing Gradio Theme
+theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
+                      text_size=gr.themes.sizes.text_lg)                
 
-# 대응 방안을 생성하는 함수
-def generate_response(prompt):
-    response = openai.Completion.create(
-        engine="davinci",  # 사용할 엔진 선택 (예: davinci, curie 등)
-        prompt=prompt,
-        max_tokens=50,  # 생성할 최대 토큰 수
-        n=1  # 생성할 텍스트 개수
-    )
+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">🔥This Huggingface Gradio Demo provides you access to GPT4 API with System Messages. Please note that you would be needing an OPENAI API key for GPT4 access🙌</h1>""")
+    gr.HTML(theme_addon_msg)
+    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
 
-    generated_text = response.choices[0].text.strip()
-    return generated_text
+    with gr.Column(elem_id = "col_container"):
+        #Users need to provide their own GPT4 API key, it is no longer provided by Huggingface 
+        with gr.Row():
+            openai_gpt4_key = gr.Textbox(label="OpenAI GPT4 Key", value="", type="password", placeholder="sk..", info = "You have to provide your own GPT4 keys for this app to function properly",)
+            with gr.Accordion(label="System message:", open=False):
+                system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour", info = system_msg_info, value="",placeholder="Type here..")
+                accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
+                          
+        chatbot = gr.Chatbot(label='GPT4', elem_id="chatbot")
+        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
+        state = gr.State([]) 
+        with gr.Row():
+            with gr.Column(scale=7):
+                b1 = gr.Button().style(full_width=True)
+            with gr.Column(scale=3):
+                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
+    
+        #top_p, temperature
+        with gr.Accordion("Parameters", open=False):
+            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
+            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
+            chat_counter = gr.Number(value=0, visible=False, precision=0)
 
-# Gradio 입력 폼 및 출력 정의
-input_text = gr.Textbox(label="텍스트 입력", placeholder="안전 사고에 대한 텍스트를 입력하세요.")
-output_label = gr.Label(label="카테고리")
-output_summary = gr.Label(label="텍스트 요약")
-output_response = gr.Textbox(label="대응 방안", readonly=True)
+    #Event handling
+    inputs.submit( predict, [openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
+    b1.click( predict, [openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
+    
+    inputs.submit(set_visible_false, [], [system_msg])
+    b1.click(set_visible_false, [], [system_msg])
+    inputs.submit(set_visible_true, [], [accordion_msg])
+    b1.click(set_visible_true, [], [accordion_msg])
+    
+    b1.click(reset_textbox, [], [inputs])
+    inputs.submit(reset_textbox, [], [inputs])
 
-# Gradio 웹 인터페이스 생성
-gr.Interface(
-    fn=classify_and_generate_response,
-    inputs=input_text,
-    outputs=[output_label, output_summary, output_response],
-    title="안전사고 분류 및 대응 방안",
-).launch()
+demo.queue(max_size=99, concurrency_count=20).launch(debug=True)