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app.py

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+import gradio as gr
+import openai
+
+openai.api_key = open("key.txt", "r").read().strip("\n")
+
+message_history = [{"role": "user", "content": f"You are a joke bot. I will specify the subject matter in my messages, and you will reply with a joke that includes the subjects I mention in my messages. Reply only with jokes to further input. If you understand, say OK."},
+                   {"role": "assistant", "content": f"OK"}]
+
+def predict(input):
+    # tokenize the new input sentence
+    message_history.append({"role": "user", "content": f"{input}"})
+
+    completion = openai.ChatCompletion.create(
+      model="gpt-3.5-turbo", #10x cheaper than davinci, and better. $0.002 per 1k tokens
+      messages=message_history
+    )
+    #Just the reply:
+    reply_content = completion.choices[0].message.content#.replace('```python', '<pre>').replace('```', '</pre>')
+
+    print(reply_content)
+    message_history.append({"role": "assistant", "content": f"{reply_content}"}) 
+    
+    # get pairs of msg["content"] from message history, skipping the pre-prompt:              here.
+    response = [(message_history[i]["content"], message_history[i+1]["content"]) for i in range(2, len(message_history)-1, 2)]  # convert to tuples of list
+    return response
+
+# creates a new Blocks app and assigns it to the variable demo.
+with gr.Blocks() as demo: 
+
+    # creates a new Chatbot instance and assigns it to the variable chatbot.
+    chatbot = gr.Chatbot() 
+
+    # creates a new Row component, which is a container for other components.
+    with gr.Row(): 
+        '''creates a new Textbox component, which is used to collect user input. 
+        The show_label parameter is set to False to hide the label, 
+        and the placeholder parameter is set'''
+        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
+    '''
+    sets the submit action of the Textbox to the predict function, 
+    which takes the input from the Textbox, the chatbot instance, 
+    and the state instance as arguments. 
+    This function processes the input and generates a response from the chatbot, 
+    which is displayed in the output area.'''
+    txt.submit(predict, txt, chatbot) # submit(function, input, output)
+    #txt.submit(lambda :"", None, txt)  #Sets submit action to lambda function that returns empty string 
+
+    '''
+    sets the submit action of the Textbox to a JavaScript function that returns an empty string. 
+    This line is equivalent to the commented out line above, but uses a different implementation. 
+    The _js parameter is used to pass a JavaScript function to the submit method.'''
+    txt.submit(None, None, txt, _js="() => {''}") # No function, no input to that function, submit action to textbox is a js function that returns empty string, so it clears immediately.
+         
+demo.launch(share=True)

key.txt

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+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