Update app.py

app.py

@@ -1,13 +1,11 @@
 import gradio as gr
 import os
 import requests
+import json
 
 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="position: absolute; top: 0; right: 0;">
@@ -21,31 +19,21 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
 
 def build_input_prompt(message, chatbot, system_prompt):
-    """
-    Constructs the input prompt string from the chatbot interactions and the current message.
-    """
-    input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n"
+    input_prompt = "\n" + system_prompt + "</s>\n\n"
     for interaction in chatbot:
-        input_prompt = input_prompt + str(interaction[0]) + "</s>\n<|assistant|>\n" + str(interaction[1]) + "\n</s>\n<|user|>\n"
+        input_prompt = input_prompt + str(interaction[0]) + "</s>\n\n" + str(interaction[1]) + "\n</s>\n\n"
 
-    input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>"
+    input_prompt = input_prompt + str(message) + "</s>\n"
     return input_prompt
 
-
 def post_request_beta(payload):
-    """
-    Sends a POST request to the predefined Zephyr-7b-Beta URL and returns the JSON response.
-    """
     response = requests.post(zephyr_7b_beta, headers=HEADERS, json=payload)
-    response.raise_for_status()  # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
+    response.raise_for_status()
     return response.json()
 
-
 def predict_beta(message, chatbot=[], system_prompt=""):
     input_prompt = build_input_prompt(message, chatbot, system_prompt)
-    data = {
-        "inputs": input_prompt
-    }
+    data = {"inputs": input_prompt}
 
     try:
         response_data = post_request_beta(data)
@@ -66,24 +54,19 @@ def predict_beta(message, chatbot=[], system_prompt=""):
         error_msg = f"Failed to decode response as JSON: {str(e)}"
         raise gr.Error(error_msg)
 
-def test_preview_chatbot(message, history):
+def chat_interface(message, history):
     response = predict_beta(message, history, SYSTEM_PROMPT)
-    text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>")
+    text_start = response.rfind("", ) + len("")
     response = response[text_start:]
     return response
 
-
-welcome_preview_message = f"""
+welcome_message = f"""
 Expand your imagination and broaden your horizons with LLM. Welcome to **{TITLE}**!:\nThis is a chatbot that can generate detailed prompts for image generation models based on simple and short user input.\nSay something like: 
 
 "{EXAMPLE_INPUT}"
 """
 
-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)
-demo2 = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview)
+chatbot_setup = gr.Chatbot(layout="panel", value=[(None, welcome_message)])
+textbox_setup = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT)
 
-demo.launch(share=True)
-demo.launch(share=True)
+gr.Interface(fn=chat_interface, inputs=textbox_setup, outputs=chatbot_setup, live=True, share=True).launch()