Create app.py

app.py

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+import os
+import openai
+import gradio as gr
+
+openai.api_key = "sk-00qmWbh6CPEJAHbT3BlbkFJLbkcfagm3tLjWZ9ukrr4"
+
+start_sequence = "\nAI:"
+restart_sequence = "\nHuman:"
+
+def predict(input, history=[]):
+    s = list(sum(history, ()))
+    s.append(input)
+
+     response = openai.Completion.create(
+        model="text-davinci-003"
+        prompt=str(s),
+        max_tokens = 1050,
+        temperature = 0.9,
+        top_p = 1,
+        frequency_penalty=0,
+        presence_penalty=0.6,
+    )
+
+    response2 = response["choices"][0]["text"]
+    history.append((input, response2))
+
+fr.Interface(fn=predict,
+            inputs="text",'state'),
+            outputs=["chatbot",'state']).launch()
+
+
+#def get_model_reply(user_input, context=[]):
+ #   context+=[user_input]
+
+#    completion = openai.Completion.create(
+#        engine="gpt-3.5-turbo", # one of the most capable models available
+#        prompt='\\n'.join([f"I am {role}.", *context])[:4096],
+#        max_tokens = 1048,
+#        temperature = 0.9,
+#        top_p = 1,
+#        frequency_penalty=0,
+#        presence_penalty=0.6,
+#    )
+    
+    # append response to context
+#    response = completion.choices[0].text.strip('\\n')
+#    context += [response]
+    
+    # list of (user, bot) responses. We will use this format later
+#    responses = [(u,b) for u,b in zip(context[::2], context[1::2])]
+    
+#    return responses, context
+   # ```
+    # defines a basic dialog interface using Gradio
+#with gr.Blocks() as dialog_app:
+ #   chatbot = gr.Chatbot() # dedicated "chatbot" component
+ #   state = gr.State([]) # session state that persists across multiple submits
+    
+ #   with gr.Row():
+ #       txt = gr.Textbox(
+ #           show_label=False, 
+ #           placeholder="Enter text and press enter"
+ #       ).style(container=False)
+
+ #   txt.submit(get_model_reply, [txt, state], [chatbot, state])
+
+#dialog_app.launch()