update app

app.py

@@ -1,143 +1,132 @@
-#import gradio as gr
-
-#def greet(name):
-#    return "Hello " + name + "!!"
-
-#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-#iface.launch()
-
-
-#import gradio as gr
-
-#key = ""
-
-#def get_key(k):
- #   global key
- ##   key = k
- #   return "Key saved successfully!"
-
-#def greet(name):
- #   return "Hello " + name + "!!"
-
-#iface = gr.Interface(fn=greet, inputs=["text", gr.inputs.Textbox()], outputs="text", 
- #                    inputs_layout="vertical", outputs_layout="vertical",
- #                    title="Greeting App", description="Enter a key and a name to greet")
-
-#iface.input[0].label = "Enter a key"  # Set the label of the first input
-#iface.input[1].label = "Enter a name"
-#iface.input[1].lines = 1
-
-#iface.buttons[0].label = "Save Key"
-#iface.buttons[0].type = "submit"
-#iface.buttons[0].onclick = get_key
-
-#iface.buttons[1].label = "Greet"
-#iface.buttons[1].type = "submit"
-#iface.buttons[1].onclick_args = {"name": iface.inputs[1].value}
-
-#iface.launch()
-
 import gradio as gr
-import openai
-
-openai.api_key= ""
-
-def get_completion(prompt, model="gpt-3.5-turbo"):
-    messages = [{"role": "user", "content": prompt}]
-    response = openai.ChatCompletion.create(
-        model=model,
-        messages=messages,
-        temperature=0, # 控制模型输出的随机程度
-    )
-    return response.choices[0].message["content"]
-
-def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
-    response = openai.ChatCompletion.create(
-        model=model,
-        messages=messages,
-        temperature=temperature, # 控制模型输出的随机程度
-    )
-#     print(str(response.choices[0].message))
-    return response.choices[0].message["content"]
-
-
-context2 = [{'role':'system', 'content':"""
-    你是订餐机器人，为披萨餐厅自动收集订单信息。\n
-    注意如果客户来了，发送了空消息，请主动与客户说你好。\n
-    你要首先问候顾客，并主动告知客户今天的菜单，询问客户今天要什么食物。\n
-    然后等待用户回复收集订单信息。收集完信息需确认顾客是否还需要添加其他内容。\n
-    最后需要询问是否自取或外送，如果是外送，你要询问地址。\n
-    最后告诉顾客订单总金额，并送上祝福。\n
-        \n
-    请确保明确所有选项、附加项和尺寸，以便从菜单中识别出该项唯一的内容。\n
-    你的回应应该以简短、非常随意和友好的风格呈现。\n
-    \n
-    菜单包括：\n
-    \n
-    菜品：\n
-    意式辣香肠披萨（大、中、小） 12.95、10.00、7.00\n
-    芝士披萨（大、中、小） 10.95、9.25、6.50\n
-    茄子披萨（大、中、小） 11.95、9.75、6.75\n
-    薯条（大、小） 4.50、3.50\n
-    希腊沙拉 7.25\n
-    \n
-    配料：\n
-    奶酪 2.00\n
-    蘑菇 1.50\n
-    香肠 3.00\n
-    加拿大熏肉 3.50\n
-    AI酱 1.50\n
-    辣椒 1.00\n
-    \n
-    饮料：\n
-    可乐（大、中、小） 3.00、2.00、1.00\n
-    雪碧（大、中、小） 3.00、2.00、1.00\n
-    瓶装水 5.00\n
-    """} ]  # accumulate messages
-
-#def get_key(k):
-#    global key
-#    key = k
-#    return "Key saved successfully!"
-
-#def greet(k,name):
-#    openai.api_key = k
-#    return "Hello " + name + "!!"
-
-#key_input = gr.inputs.Textbox(label="Enter a key")
-#key_button = gr.Button(label="Save Key", type="submit", onclick=get_key(key_input))
-
-#name_input = gr.inputs.Textbox(label="Enter a name")
-#greet_button = gr.Button(label="Greet", type="submit", onclick_args={"name": name_input})
-
-
-
-
-
-
-
-#iface = gr.Interface(fn=greet, inputs=[key_input, name_input], outputs="text", 
-#                     inputs_layout="vertical", outputs_layout="vertical",
-#                     title="Greeting App", description="Enter a key and a name to greet")
-
-#iface.launch()
-
-key_input = gr.inputs.Textbox(label="Enter a key")
-chat_input = gr.inputs.Textbox(label="Enter a chattext")
-
-def chatbot_interface_restaurant_chinese(k_text,t_text):
-    openai.api_key = k_text
-    if not hasattr(chatbot_interface, "chat_history"):
-        chatbot_interface.chat_history = ""
-    context2.append({'role':'user', 'content':f"{t_text}"})
-    chatbot_interface.chat_history += "User: " + t_text + "\n"
-    output_text = get_completion_from_messages(context2) 
-    context2.append({'role':'assistant', 'content':f"{output_text}"})
-    chatbot_interface.chat_history += "assistant: " + output_text + "\n\n"
-    return chatbot_interface.chat_history
-
-iface = gr.Interface(fn=greet, inputs=[key_input, chat_input], outputs="text", 
-                    inputs_layout="vertical", outputs_layout="vertical",
-                   title="Greeting App", description="Enter a key and a name to greet")
-
-iface.launch()
+import os 
+import json 
+import requests
+
+#Streaming endpoint 
+API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
+
+#Testing with my Open AI Key 
+#OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
+
+def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]):  #repetition_penalty, top_k
+
+    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}"
+    }
+
+    print(f"chat_counter - {chat_counter}")
+    if chat_counter != 0 :
+        messages=[]
+        for data in chatbot:
+          temp1 = {}
+          temp1["role"] = "user" 
+          temp1["content"] = data[0] 
+          temp2 = {}
+          temp2["role"] = "assistant" 
+          temp2["content"] = data[1]
+          messages.append(temp1)
+          messages.append(temp2)
+        temp3 = {}
+        temp3["role"] = "user" 
+        temp3["content"] = inputs
+        messages.append(temp3)
+        #messages
+        payload = {
+        "model": "gpt-3.5-turbo",
+        "messages": messages, #[{"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,
+        }
+
+    chat_counter+=1
+
+    history.append(inputs)
+    print(f"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)
+    #response = requests.post(API_URL, headers=headers, json=payload, stream=True)
+    token_counter = 0 
+    partial_words = "" 
+
+    counter=0
+    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']:
+              #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
+              #  break
+              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  # resembles {chatbot: chat, state: 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 gpt-3.5-turbo LLM.
+"""
+                
+with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
+                #chatbot {height: 520px; overflow: auto;}""") as demo:
+    gr.HTML(title)
+    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=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",)
+            #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)
+
+    inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
+    b1.click( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
+    b1.click(reset_textbox, [], [inputs])
+    inputs.submit(reset_textbox, [], [inputs])
+                    
+    #gr.Markdown(description)
+    demo.queue().launch(debug=True)

app1.py

@@ -0,0 +1,143 @@
+#import gradio as gr
+
+#def greet(name):
+#    return "Hello " + name + "!!"
+
+#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+#iface.launch()
+
+
+#import gradio as gr
+
+#key = ""
+
+#def get_key(k):
+ #   global key
+ ##   key = k
+ #   return "Key saved successfully!"
+
+#def greet(name):
+ #   return "Hello " + name + "!!"
+
+#iface = gr.Interface(fn=greet, inputs=["text", gr.inputs.Textbox()], outputs="text", 
+ #                    inputs_layout="vertical", outputs_layout="vertical",
+ #                    title="Greeting App", description="Enter a key and a name to greet")
+
+#iface.input[0].label = "Enter a key"  # Set the label of the first input
+#iface.input[1].label = "Enter a name"
+#iface.input[1].lines = 1
+
+#iface.buttons[0].label = "Save Key"
+#iface.buttons[0].type = "submit"
+#iface.buttons[0].onclick = get_key
+
+#iface.buttons[1].label = "Greet"
+#iface.buttons[1].type = "submit"
+#iface.buttons[1].onclick_args = {"name": iface.inputs[1].value}
+
+#iface.launch()
+
+import gradio as gr
+import openai
+
+openai.api_key= ""
+
+def get_completion(prompt, model="gpt-3.5-turbo"):
+    messages = [{"role": "user", "content": prompt}]
+    response = openai.ChatCompletion.create(
+        model=model,
+        messages=messages,
+        temperature=0, # 控制模型输出的随机程度
+    )
+    return response.choices[0].message["content"]
+
+def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
+    response = openai.ChatCompletion.create(
+        model=model,
+        messages=messages,
+        temperature=temperature, # 控制模型输出的随机程度
+    )
+#     print(str(response.choices[0].message))
+    return response.choices[0].message["content"]
+
+
+context2 = [{'role':'system', 'content':"""
+    你是订餐机器人，为披萨餐厅自动收集订单信息。\n
+    注意如果客户来了，发送了空消息，请主动与客户说你好。\n
+    你要首先问候顾客，并主动告知客户今天的菜单，询问客户今天要什么食物。\n
+    然后等待用户回复收集订单信息。收集完信息需确认顾客是否还需要添加其他内容。\n
+    最后需要询问是否自取或外送，如果是外送，你要询问地址。\n
+    最后告诉顾客订单总金额，并送上祝福。\n
+        \n
+    请确保明确所有选项、附加项和尺寸，以便从菜单中识别出该项唯一的内容。\n
+    你的回应应该以简短、非常随意和友好的风格呈现。\n
+    \n
+    菜单包括：\n
+    \n
+    菜品：\n
+    意式辣香肠披萨（大、中、小） 12.95、10.00、7.00\n
+    芝士披萨（大、中、小） 10.95、9.25、6.50\n
+    茄子披萨（大、中、小） 11.95、9.75、6.75\n
+    薯条（大、小） 4.50、3.50\n
+    希腊沙拉 7.25\n
+    \n
+    配料：\n
+    奶酪 2.00\n
+    蘑菇 1.50\n
+    香肠 3.00\n
+    加拿大熏肉 3.50\n
+    AI酱 1.50\n
+    辣椒 1.00\n
+    \n
+    饮料：\n
+    可乐（大、中、小） 3.00、2.00、1.00\n
+    雪碧（大、中、小） 3.00、2.00、1.00\n
+    瓶装水 5.00\n
+    """} ]  # accumulate messages
+
+#def get_key(k):
+#    global key
+#    key = k
+#    return "Key saved successfully!"
+
+#def greet(k,name):
+#    openai.api_key = k
+#    return "Hello " + name + "!!"
+
+#key_input = gr.inputs.Textbox(label="Enter a key")
+#key_button = gr.Button(label="Save Key", type="submit", onclick=get_key(key_input))
+
+#name_input = gr.inputs.Textbox(label="Enter a name")
+#greet_button = gr.Button(label="Greet", type="submit", onclick_args={"name": name_input})
+
+
+
+
+
+
+
+#iface = gr.Interface(fn=greet, inputs=[key_input, name_input], outputs="text", 
+#                     inputs_layout="vertical", outputs_layout="vertical",
+#                     title="Greeting App", description="Enter a key and a name to greet")
+
+#iface.launch()
+
+key_input = gr.inputs.Textbox(label="Enter a key")
+chat_input = gr.inputs.Textbox(label="Enter a chattext")
+
+def chatbot_interface_restaurant_chinese(k_text,t_text):
+    openai.api_key = k_text
+    if not hasattr(chatbot_interface, "chat_history"):
+        chatbot_interface.chat_history = ""
+    context2.append({'role':'user', 'content':f"{t_text}"})
+    chatbot_interface.chat_history += "User: " + t_text + "\n"
+    output_text = get_completion_from_messages(context2) 
+    context2.append({'role':'assistant', 'content':f"{output_text}"})
+    chatbot_interface.chat_history += "assistant: " + output_text + "\n\n"
+    return chatbot_interface.chat_history
+
+iface = gr.Interface(fn=greet, inputs=[key_input, chat_input], outputs="text", 
+                    inputs_layout="vertical", outputs_layout="vertical",
+                   title="Greeting App", description="Enter a key and a name to greet")
+
+iface.launch()