Update app.py

app.py

@@ -7,6 +7,7 @@ css_file = os.path.join(current_dir, "style.css")
 
 initial_prompt = "You are a helpful assistant."
 
+
 def parse_text(text):
     lines = text.split("\n")
     for i,line in enumerate(lines):
@@ -23,103 +24,105 @@ def parse_text(text):
                 lines[i] = '<br/>'+line.replace(" ", "&nbsp;")
     return "".join(lines)
 
+
 def get_response(system, context, raw=False):
     openai.api_key = "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"
     response = openai.Completion.create(
         engine="text-davinci-002",
-        prompt=f"{system}\n\n{context}",
-        temperature=0.5,
+        prompt=f"{system}{''.join([f'{c['role']}: {c['content']}\n' for c in context])}",
         max_tokens=1024,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0
+        n=1,
+        stop=None,
+        temperature=0.5,
     )
 
-    if raw:
-        return response
-    else:
-        message = response.choices[0].text.strip()
-        message_with_stats = f'{message}'
-        return message, parse_text(message_with_stats)
+    message = response.choices[0].text
+    message_with_stats = f"{message}"
+    return message, parse_text(message_with_stats)
+
 
-def predict(chatbot, input_sentence, system, context):
+def predict(input_sentence):
     if len(input_sentence) == 0:
         return []
-    context.append(input_sentence)
-
-    message, message_with_stats = get_response(system, context)
 
     chatbot.append((input_sentence, message_with_stats))
 
-    context.append(message)
+    context.append({"role": "user", "content": f"{input_sentence}"})
+
+    message, message_with_stats = get_response(systemPrompt.value["content"], context)
+
+    context.append({"role": "assistant", "content": message})
 
     return chatbot, context
 
-def retry(chatbot, system, context):
+
+def retry():
     if len(context) == 0:
         return [], []
-    context.pop()
-    chatbot.pop()
+
+    context[-1]["content"] = "Could you rephrase that?"
+
+    message, message_with_stats = get_response(systemPrompt.value["content"], context[:-1])
+
+    context[-1] = {"role": "assistant", "content": message}
+
+    chatbot[-1] = (context[-2]["content"], message_with_stats)
 
     return chatbot, context
 
-def delete_last_conversation(chatbot, context):
+
+def delete_last_conversation():
     if len(context) == 0:
         return [], []
-    chatbot.pop()
-    context.pop()
-    context.pop()
 
+    chatbot = chatbot[:-1]
+    context = context[:-2]
     return chatbot, context
 
-def reduce_token(chatbot, system, context):
-    if len(context) == 0:
-        return [], []
-    context.pop()
 
-    context.append("Please help me summarize our conversation to reduce token usage. Don't include this sentence in the summary.")
-    message, message_with_stats = get_response(system, context, raw=True)
-    summary = message.choices[0].text.strip()
+def reduce_token():
+    context.append({"role": "user", "content": "Please summarize our conversation and reduce tokens used. Don't include this prompt."})
 
-    statistics = f'This conversation token usage [{message.total_tokens} / 2048] (Prompt: {message.prompt_length}, Response: {message.choices[0].length})'
+    response = get_response(systemPrompt.value["content"], context, raw=True)
 
-    chatbot.append(("Please help me summarize our conversation to reduce token usage.", summary + statistics))
+    optmz_str = f'Okay, we talked about: {response.choices[0].text}\n\nTotal tokens used this conversation: {response.choices[0].logprobs.top_logprobs[0].tokens}'
 
-    context.append(f"We talked about {summary}")
+    chatbot.append(("Please summarize our conversation and reduce tokens used. Don't include this prompt.", parse_text(optmz_str)))
+    context = [{"role": "assistant", "content": f"Okay, we talked about: {response.choices[0].text}"}]
     return chatbot, context
 
+
 def reset_state():
     return [], []
 
+
 def update_system(new_system_prompt):
-    return new_system_prompt
+    return {"role": "system", "content": new_system_prompt}
 
-title = """<h1 align="center">You ask, I answer.</h1>"""
-description = """<div align=center>
 
-Not interested in describing your needs to ChatGPT? Use [ChatGPT Shortcut](https://newzone.top/chatgpt/)
+title = """<h1 align="center">You Ask, I Answer - Chatbot</h1>"""
 
-</div>
-"""
+description = "This chatbot is designed to assist you with any questions or tasks you may have. Simply type in your query and the chatbot will provide you with a helpful response."
 
-#with gr.blocks() as demo:
-    gr.html(title)
-    chatbot = []
-    context = [initial_prompt]
-    system = initial_prompt
+systemPrompt = gr.inputs.Textbox(lines=2, label="Enter the system prompt you would like to use:")
+userInput = gr.inputs.Textbox(lines=2, label="Enter your message:")
 
-    input_text = gr.inputs.Textbox(lines=1, placeholder="Enter your message here...")
-    chat_history = gr.outputs.HTML(markdown=False)
+chatbot_output = gr.outputs.HTML(type="auto")
 
-    gradio_ui = gr.Interface(fn=lambda message: predict(chatbot, message, system, context),
-                             inputs=input_text,
-                             outputs=chat_history,
-                             title=title,
-                             description=description,
-                             theme="compact",
-                             allow_flagging=False,
-                             layout="vertical",
-                             css=css_file,
-                             )
+chatbot_interface = gr.Interface(
+predict,
+[systemPrompt, userInput],
+chatbot_output,
+title=title,
+description=description,
+theme="compact",
+layout="vertical",
+examples=[
+["Can you help me with my math homework?", "Sure, what do you need help with?"],
+["How can I make pizza from scratch?", "First, you will need to gather the ingredients..."]
+],
+article="https://openai.com/blog/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2021/"
+)
 
-    gradio_ui.launch()
+if name == "main":
+chatbot_interface.launch(debug=True)