Update app.py

app.py

@@ -11,8 +11,8 @@ KEY = os.environ.get("KEY")
 
 SPECIAL_SYMBOLS = ["⠀", "⠀"] # ["‹", "›"] ['"', '"']
 
-DEFAULT_INPUT = f"User: Hi!"
-DEFAULT_PREOUTPUT = f"Statical: "
+DEFAULT_INPUT = "User: Hi!"
+DEFAULT_WRAP = "Statical: %s"
 DEFAULT_INSTRUCTION = "Statical is a helpful chatbot who is communicating with people."
 
 DEFAULT_STOPS = '["⠀", "⠀"]' # '["‹", "›"]' '[\"\\\"\"]'
@@ -31,13 +31,13 @@ for model_name, model_endpoint in API_ENDPOINTS.items():
     CHOICES.append(model_name)
     CLIENTS[model_name] = InferenceClient(model_endpoint, headers = { "Authorization": f"Bearer {API_TOKEN}" })
 
-def format(instruction, history, input, preoutput):
+def format(instruction, history, input, wrap):
     sy_l, sy_r = SPECIAL_SYMBOLS[0], SPECIAL_SYMBOLS[1]
     formatted_history = "".join(f"{sy_l}{message[0]}{sy_r}\n{sy_l}{message[1]}{sy_r}\n" for message in history)
     formatted_input = f"{sy_l}INSTRUCTIONS: {instruction}{sy_r}\n{formatted_history}{sy_l}{input}{sy_r}\n{sy_l}"
-    return f"{formatted_input}{preoutput}", formatted_input
+    return f"{formatted_input}{wrap % ("")}", formatted_input
     
-def predict(access_key, instruction, history, input, preoutput, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed):
+def predict(access_key, instruction, history, input, wrap, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed):
 
     if (access_key != KEY):
         print(">>> MODEL FAILED: Input: " + input + ", Attempted Key: " + access_key)
@@ -46,12 +46,12 @@ def predict(access_key, instruction, history, input, preoutput, model, temperatu
     instruction = instruction or DEFAULT_INSTRUCTION
     history = history or []
     input = input or ""
-    preoutput = preoutput or ""
+    wrap = wrap or ""
     stop_seqs = stop_seqs or DEFAULT_STOPS
         
     stops = json.loads(stop_seqs)
 
-    formatted_input, formatted_input_base = format(instruction, history, input, preoutput)
+    formatted_input, formatted_input_base = format(instruction, history, input, wrap)
     print(seed)
     print(formatted_input)
     response = CLIENTS[model].text_generation(
@@ -70,7 +70,7 @@ def predict(access_key, instruction, history, input, preoutput, model, temperatu
     )
 
     sy_l, sy_r = SPECIAL_SYMBOLS[0], SPECIAL_SYMBOLS[1]
-    result = preoutput + response
+    result = wrap % (response)
     
     for stop in stops:
         result = result.split(stop, 1)[0]
@@ -93,13 +93,13 @@ def maintain_cloud():
 
 with gr.Blocks() as demo:
     with gr.Row(variant = "panel"):
-        gr.Markdown("🔯 This is a private LLM CHAT Space owned within STC Holdings!\n\n\nhttps://discord.gg/6JRtGawz7B")
+        gr.Markdown("✡️ This is a private LLM CHAT Space owned within STC Holdings!\n\n\nhttps://discord.gg/6JRtGawz7B")
             
     with gr.Row():
         with gr.Column():
             history = gr.Chatbot(abel = "History", elem_id = "chatbot")
             input = gr.Textbox(label = "Input", value = DEFAULT_INPUT, lines = 2)
-            preoutput = gr.Textbox(label = "Pre-Output", value = DEFAULT_PREOUTPUT, lines = 1)
+            wrap = gr.Textbox(label = "Wrap", value = DEFAULT_WRAP, lines = 1)
             instruction = gr.Textbox(label = "Instruction", value = DEFAULT_INSTRUCTION, lines = 4)
             access_key = gr.Textbox(label = "Access Key", lines = 1)
             run = gr.Button("▶")
@@ -120,7 +120,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             output = gr.Textbox(label = "Output", value = "", lines = 50)
 
-    run.click(predict, inputs = [access_key, instruction, history, input, preoutput, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed], outputs = [output, input, history])
+    run.click(predict, inputs = [access_key, instruction, history, input, wrap, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed], outputs = [output, input, history])
     clear.click(clear_history, [], history)
     cloud.click(maintain_cloud, inputs = [], outputs = [input, output])