Update app.py

app.py

@@ -5,6 +5,7 @@ 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 Man Riding A Horse in Space"
+zephyr_3b = "https://huggingface.co/chat/models/llama/llama-3b"
 
 HF_TOKEN = os.getenv("HF_TOKEN")
 HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
@@ -13,38 +14,38 @@ 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 + " \n \n"
+    input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n"
     for interaction in chatbot:
-        input_prompt = input_prompt + str(interaction[0]) + " \n \n" + str(interaction[1]) + "\n \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) + " \n "
+    input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>"
     return input_prompt
 
 
-def post_request(model_url, payload):
+def post_request_beta(payload):
     """
-    Sends a POST request to the specified model URL and returns the JSON response.
+    Sends a POST request to the predefined Zephyr-7b-Beta URL and returns the JSON response.
     """
-    response = requests.post(model_url, headers=HEADERS, json=payload)
+    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
     return response.json()
 
 
-def predict(model_url, message, chatbot=[], system_prompt=""):
+def predict_beta(message, chatbot=[], system_prompt=""):
     input_prompt = build_input_prompt(message, chatbot, system_prompt)
     data = {
         "inputs": input_prompt
     }
 
     try:
-        response_data = post_request(model_url, data)
+        response_data = post_request_beta(data)
         json_obj = response_data[0]
         
         if 'generated_text' in json_obj and len(json_obj['generated_text']) > 0:
             bot_message = json_obj['generated_text']
             return bot_message
         elif 'error' in json_obj:
-            raise gr.Error(json_obj['error'] +'Please refresh and try again with smaller input prompt')
+            raise gr.Error(json_obj['error'] + ' Please refresh and try again with smaller input prompt')
         else:
             warning_msg = f"Unexpected response: {json_obj}"
             raise gr.Error(warning_msg)
@@ -55,10 +56,9 @@ def predict(model_url, 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, model_url):
-    response = predict(model_url, message, history, SYSTEM_PROMPT)
-    text_start = response.rfind(" ", ) + len(" ")
+def test_preview_chatbot(message, history):
+    response = predict_beta(message, history, SYSTEM_PROMPT)
+    text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>")
     response = response[text_start:]
     return response
 
@@ -68,16 +68,7 @@ Expand your imagination and broaden your horizons with LLM. Welcome to **{TITLE}
 "{EXAMPLE_INPUT}"
 """
 
-model_url_input = gr.Textbox(label="Model URL", value="https://huggingface.co/chat/models/meta-llama/Meta-Llama-3-70B-Instruct")
 chatbot_preview = gr.Chatbot(layout="panel", value=[(None, welcome_preview_message)])
 textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT)
 
-demo = gr.Interface(
-    fn=test_preview_chatbot, 
-    inputs=["text", "state", "text"], 
-    outputs="text", 
-    title=TITLE, 
-    description="Image Prompter"
-)
-
-demo.launch()
+demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview)