Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import os
 import requests
+from gradio import Error
 
 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"
@@ -10,24 +11,16 @@ 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_prompt + "\n\n" + message
     return input_prompt
 
-
 def post_request(model_url, payload):
-    """
-    Sends a POST request to the specified model URL and returns the JSON response.
-    """
     response = requests.post(model_url, 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(model_url, message, chatbot=[], system_prompt=""):
-    input_prompt = build_input_prompt(message, chatbot, system_prompt)
+def predict(model_url, message, system_prompt):
+    input_prompt = build_input_prompt(message, [], system_prompt)
     data = {
         "prompt": input_prompt,
         "max_new_tokens": 256,
@@ -41,18 +34,16 @@ def predict(model_url, message, chatbot=[], system_prompt=""):
         return bot_message
     except requests.HTTPError as e:
         error_msg = f"Request failed with status code {e.response.status_code}"
-        raise gr.Error(error_msg)
+        raise Error(error_msg)
     except json.JSONDecodeError as e:
         error_msg = f"Failed to decode response as JSON: {str(e)}"
-        raise gr.Error(error_msg)
+        raise Error(error_msg)
 
-
-def test_preview_chatbot(message, history):
+def test_preview_chatbot(message):
     model_url = "https://huggingface.co/chat/models/llama/llama-3b"
-    response = predict(model_url, message, history, SYSTEM_PROMPT)
+    response = predict(model_url, message, SYSTEM_PROMPT)
     return response
 
-
 welcome_preview_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}"
@@ -63,7 +54,7 @@ textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT)
 
 demo = gr.Interface(
     fn=test_preview_chatbot, 
-    inputs=["text", "state"], 
+    inputs="text", 
     outputs="text", 
     title=TITLE, 
     description="Image Prompter"