Update app.py

app.py

@@ -6,16 +6,6 @@ import re
 
 API_TOKEN = os.environ.get("API_TOKEN")
 
-KEY = os.environ.get("KEY")
-
-SPECIAL_SYMBOLS = ["⠀", "⠀"] # ["‹", "›"] ['"', '"']
-
-DEFAULT_INPUT = "User: Hi!"
-DEFAULT_WRAP = "Statical: %s"
-DEFAULT_INSTRUCTION = "Statical is a helpful chatbot who is communicating with people."
-
-DEFAULT_STOPS = '["⠀", "⠀"]' # '["‹", "›"]' '[\"\\\"\"]'
-
 API_ENDPOINTS = {
     "Falcon": "tiiuae/falcon-180B-chat",
     "Llama": "meta-llama/Llama-2-70b-chat-hf",
@@ -33,35 +23,11 @@ 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, wrap):
-    sy_l, sy_r = SPECIAL_SYMBOLS[0], SPECIAL_SYMBOLS[1]
-    wrapped_input = wrap % ("")
-    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}{wrapped_input}", formatted_input
-    
-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)
-        return ("[UNAUTHORIZED ACCESS]", input, []);
-
-    instruction = instruction or DEFAULT_INSTRUCTION
-    history = history or []
-    input = input or ""
-    wrap = wrap or ""
-    stop_seqs = stop_seqs or DEFAULT_STOPS
-        
+def predict(input, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed):
     stops = json.loads(stop_seqs)
-
-    formatted_input, formatted_input_base = format(instruction, history, input, wrap)
-    
-    print(seed)
-    print(formatted_input)
-    print(model)
-
+   
     response = CLIENTS[model].text_generation(
-        formatted_input,
+        input,
         temperature = temperature,
         max_new_tokens = max_tokens,
         top_p = top_p,
@@ -75,27 +41,7 @@ def predict(access_key, instruction, history, input, wrap, model, temperature, t
         return_full_text = False
     )
 
-    sy_l, sy_r = SPECIAL_SYMBOLS[0], SPECIAL_SYMBOLS[1]
-    result = wrap % (response)
-    
-    for stop in stops:
-        result = result.split(stop, 1)[0]
-    for symbol in stops:
-        result = result.replace(symbol, '')
-
-    history = history + [[input, result]]
-
-    print(f"---\nUSER: {input}\nBOT: {result}\n---")
-
-    return (result, input, history)
-
-def clear_history():
-    print(">>> HISTORY CLEARED!")
-    return []
-     
-def maintain_cloud():
-    print(">>> SPACE MAINTAINED!")
-    return ("SUCCESS!", "SUCCESS!")
+    return response
 
 with gr.Blocks() as demo:
     with gr.Row(variant = "panel"):
@@ -103,14 +49,9 @@ with gr.Blocks() as demo:
             
     with gr.Row():
         with gr.Column():
-            history = gr.Chatbot(label = "History", elem_id = "chatbot")
             input = gr.Textbox(label = "Input", value = DEFAULT_INPUT, lines = 2)
-            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("▶")
             clear = gr.Button("🗑️")
-            cloud = gr.Button("☁️")
             
         with gr.Column():
             model = gr.Dropdown(choices = CHOICES, value = next(iter(API_ENDPOINTS)), interactive = True, label = "Model")
@@ -119,15 +60,14 @@ with gr.Blocks() as demo:
             top_k = gr.Slider( minimum = 1, maximum = 2048, value = 50, step = 1, interactive = True, label = "Top K" )
             rep_p = gr.Slider( minimum = 0.01, maximum = 2, value = 1.2, step = 0.01, interactive = True, label = "Repetition Penalty" )
             max_tokens = gr.Slider( minimum = 1, maximum = 2048, value = 32, step = 64, interactive = True, label = "Max New Tokens" )
-            stop_seqs = gr.Textbox( value = DEFAULT_STOPS, interactive = True, label = "Stop Sequences ( JSON Array / 4 Max )" )
+            stop_seqs = gr.Textbox( value = "", interactive = True, label = "Stop Sequences ( JSON Array / 4 Max )" )
             seed = gr.Slider( minimum = 0, maximum = 9007199254740991, value = 42, step = 1, interactive = True, label = "Seed" )
             
     with gr.Row():
         with gr.Column():
             output = gr.Textbox(label = "Output", value = "", lines = 50)
 
-    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], queue = False)
+    run.click(predict, inputs = [input, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed], outputs = [output], queue = False)
     clear.click(clear_history, [], history, queue = False)
-    cloud.click(maintain_cloud, inputs = [], outputs = [input, output], queue = False)
     
 demo.launch(show_api = True)