Update app.py

app.py

@@ -1,18 +1,15 @@
-import gradio as gr
-import os
-import requests
-
 SYSTEM_PROMPT = "As an LLM, your job is to generate detailed prompts that start with generate the image, for image generation models based on user input. Be descriptive and specific, but also make sure your prompts are clear and concise."
 TITLE = "Image Prompter"
 EXAMPLE_INPUT = "A Reflective cat between stars."
+import gradio as gr
+import os
+import requests
 
 html_temp = """
-<div style="text-align: center;">
-    <h1>{}</h1>
-    <img src='https://huggingface.co/spaces/NerdN/open-gpt-Image-Prompter/blob/main/_45a03b4d-ea0f-4b81-873d-ff6b10461d52.jpg' alt='Your Image' style='width:300px;height:300px;'>
-    <p>{}</p>
+<div style="position: absolute; top: 0; right: 0;">
+    <img src='https://huggingface.co/spaces/NerdN/open-gpt-Image-Prompter/blob/main/_45a03b4d-ea0f-4b81-873d-ff6b10461d52.jpg' alt='Your Image' style='width:100px;height:100px;'>
 </div>
-""".format(TITLE, EXAMPLE_INPUT)
+"""
 
 zephyr_7b_beta = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta/"
 
@@ -23,11 +20,11 @@ def build_input_prompt(message, chatbot, system_prompt):
     """
     Constructs the input prompt string from the chatbot interactions and the current message.
     """
-    input_prompt = "\n" + system_prompt + "</s>\n\n"
+    input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n"
     for interaction in chatbot:
-        input_prompt = input_prompt + str(interaction[0]) + "</s>\n\n" + str(interaction[1]) + "\n</s>\n\n"
+        input_prompt = input_prompt + str(interaction[0]) + "</s>\n<|assistant|>\n" + str(interaction[1]) + "\n</s>\n<|user|>\n"
 
-    input_prompt = input_prompt + str(message) + "</s>\n"
+    input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>"
     return input_prompt
 
 
@@ -67,7 +64,7 @@ def predict_beta(message, chatbot=[], system_prompt=""):
 
 def test_preview_chatbot(message, history):
     response = predict_beta(message, history, SYSTEM_PROMPT)
-    text_start = response.rfind("", ) + len("")
+    text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>")
     response = response[text_start:]
     return response
 
@@ -81,6 +78,7 @@ Expand your imagination and broaden your horizons with LLM. Welcome to **{TITLE}
 chatbot_preview = gr.Chatbot(layout="panel", value=[(None, welcome_preview_message)])
 textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT)
 
-demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview, title=None, html=html_temp)
+demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview)
+
 
-demo.launch(share=True)
+demo.launch(share=True)