Update app.py

app.py

@@ -1,138 +1,54 @@
 import gradio as gr
 import openai
 import os
-import sys
-import kivy
-# import markdown
+
+css_file = os.path.join(os.path.dirname(file), "style.css")
 
 initial_prompt = "You are a helpful assistant."
 
 def parse_text(text):
-    lines = text.split("\n")
-    for i,line in enumerate(lines):
-        if "```" in line:
-            items = line.split('`')
-            if items[-1]:
-                lines[i] = f'<pre><code class="{items[-1]}">'
-            else:
-                lines[i] = f'</code></pre>'
-        else:
-            if i>0:
-                line = line.replace("<", "&lt;")
-                line = line.replace(">", "&gt;")
-                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.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[system, *context],
-    )
-    if raw:
-        return response
-    else:
-        statistics = f'This conversation Tokens usage【{response["usage"]["total_tokens"]} / 4096】 （ Question + above {response["usage"]["prompt_tokens"]}，Answer {response["usage"]["completion_tokens"]} ）'
-        message = response["choices"][0]["message"]["content"]
-
-        message_with_stats = f'{message}'
-#         message_with_stats = markdown.markdown(message_with_stats)
-
-        return message, parse_text(message_with_stats)
-        #return message
-
-def predict(chatbot, input_sentence, system, context):
-    if len(input_sentence) == 0:
-        return []
-    context.append({"role": "user", "content": f"{input_sentence}"})
-
-    message, message_with_stats = get_response(system, context)
-
-    context.append({"role": "assistant", "content": message})
-
-    chatbot.append((input_sentence, message_with_stats))
-
-    return chatbot, context
-
-def retry(chatbot, system, context):
-    if len(context) == 0:
-        return [], []
-    message, message_with_stats = get_response(system, 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):
-    if len(context) == 0:
-        return [], []
-    chatbot = chatbot[:-1]
-    context = context[:-2]
-    return chatbot, context
-
-def reduce_token(chatbot, system, context):
-    context.append({"role": "user", "content": "请帮我总结一下上述对话的内容，实现减少tokens的同时，保证对话的质量。在总结中不要加入这一句话。"})
-
-    response = get_response(system, context, raw=True)
-
-    statistics = f'本次对话Tokens用量【{response["usage"]["completion_tokens"]+12+12+8} / 4096】'
-    optmz_str = markdown.markdown( f'好的，我们之前聊了:{response["choices"][0]["message"]["content"]}\n\n================\n\n{statistics}' )
-    chatbot.append(("请帮我总结一下上述对话的内容，实现减少tokens的同时，保证对话的质量。", optmz_str))
-    
-    context = []
-    context.append({"role": "user", "content": "我们之前聊了什么?"})
-    context.append({"role": "assistant", "content": f'我们之前聊了：{response["choices"][0]["message"]["content"]}'})
-    return chatbot, context
-
-
-def reset_state():
-    return [], []
-
-def update_system(new_system_prompt):
-    return {"role": "system", "content": new_system_prompt}
-
-title = """<h1 align="center">Tu întrebi și eu răspund.</h1>"""
-description = """<div align=center>
-
-Will not describe your needs to ChatGPT？You Use [ChatGPT Shortcut](https://newzone.top/chatgpt/)
-
-</div>
-"""
-
-with gr.Blocks() as demo:
-    gr.HTML(title)
-    chatbot = gr.Chatbot().style(color_map=("#A238FF", "#A238FF"))
-    context = gr.State([])
-    systemPrompt = gr.State(update_system(initial_prompt))
-
-    with gr.Row():
-        with gr.Column(scale=12):
-            txt = gr.Textbox(show_label=False, placeholder="Please enter any of your needs here").style(container=False)
-        with gr.Column(min_width=50, scale=1):
-            #submitBtn = gr.Button("🚀 Submit", variant="Primary")
-            submitBtn = gr.Button("🚀 Submit").style(css={"background-color": "#A238FF"})
-    with gr.Row():
-        emptyBtn = gr.Button("🧹 New conversation")
-        retryBtn = gr.Button("🔄 Resubmit")
-        delLastBtn = gr.Button("🗑️ Delete conversation")
-        #reduceTokenBtn = gr.Button("♻️ Optimize Tokens")
-
-    #newSystemPrompt = gr.Textbox(show_label=True, placeholder=f"Setting System Prompt...", label="Change System prompt").style(container=True)
-    #systemPromptDisplay = gr.Textbox(show_label=True, value=initial_prompt, interactive=False, label="Current System prompt").style(container=True)
-    
-    #gr.Markdown(description)
-    
-    txt.submit(predict, [chatbot, txt, systemPrompt, context], [chatbot, context], show_progress=True)
-    txt.submit(lambda :"", None, txt)
-    submitBtn.click(predict, [chatbot, txt, systemPrompt, context], [chatbot, context], show_progress=True)
-    submitBtn.click(lambda :"", None, txt)
-    emptyBtn.click(reset_state, outputs=[chatbot, context])
-    #newSystemPrompt.submit(update_system, newSystemPrompt, systemPrompt)
-    #newSystemPrompt.submit(lambda x: x, newSystemPrompt, systemPromptDisplay)
-    #newSystemPrompt.submit(lambda :"", None, newSystemPrompt)
-    retryBtn.click(retry, [chatbot, systemPrompt, context], [chatbot, context], show_progress=True)
-    delLastBtn.click(delete_last_conversation, [chatbot, context], [chatbot, context], show_progress=True)
-    #reduceTokenBtn.click(reduce_token, [chatbot, systemPrompt, context], [chatbot, context], show_progress=True)
-
-
-demo.launch()
+lines = text.split("\n")
+for i,line in enumerate(lines):
+if "" in line: items = line.split('`') if items[-1]: lines[i] = f'<pre><code class="{items[-1]}">' else: lines[i]= '<pre><code>' elif "" in line and "</code></pre>" not in line:
+lines[i] = "</code></pre>"
+elif "#" in line:
+lines[i] = f"<h1>{line.strip('#')}</h1>"
+elif "##" in line:
+lines[i] = f"<h2>{line.strip('##')}</h2>"
+elif "###" in line:
+lines[i] = f"<h3>{line.strip('###')}</h3>"
+return "\n".join(lines)
+
+def generate_response(prompt):
+# procesăm promptul
+prompt = parse_text(prompt)
+# configurăm OpenAI API
+openai.api_key = "API_KEY_HERE"
+model_engine = "davinci"
+prompt_length = len(prompt)
+max_tokens = 1024 - prompt_length
+temperature = 0.7
+# generăm răspunsul folosind OpenAI API
+response = openai.Completion.create(
+engine=model_engine,
+prompt=prompt,
+max_tokens=max_tokens,
+n=1,
+temperature=temperature,
+)
+message = response.choices[0].text.strip()
+# procesăm răspunsul generat
+message = parse_text(message)
+return message
+
+definim interfața Gradio
+iface = gr.Interface(
+fn=generate_response,
+inputs=gr.inputs.Textbox(lines=7, label="Input prompt"),
+outputs=gr.outputs.HTML(label="Model response"),
+title="OpenAI GPT-3 assistant",
+layout="vertical",
+width=800,
+css=css_file,
+)
+iface.launch()