Update app.py

app.py

@@ -4,18 +4,22 @@ os.system('CMAKE_ARGS="-DLLAMA_OPENBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-
 import wget
 from llama_cpp import Llama
 import random
-
 url = 'https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML/resolve/main/WizardLM-7B-uncensored.ggmlv3.q2_K.bin'
 filename = wget.download(url)
 llm2 = Llama(model_path=filename, seed=random.randint(1, 2**31))
-
+filename = wget.download(url)
+theme = gr.themes.Soft(
+    primary_hue=gr.themes.Color("#ededed", "#fee2e2", "#fecaca", "#fca5a5", "#f87171", "#ef4444", "#dc2626", "#b91c1c", "#991b1b", "#7f1d1d", "#6c1e1e"),
+    neutral_hue="red",
+)
 title = """<h1 align="center">Chat with awesome WizardLM 7b model!</h1><br>"""
-with gr.Blocks(theme=gr.themes.clean) as demo:
+with gr.Blocks(theme=theme) as demo:
     gr.HTML(title)
-    gr.HTML("This model is awesome for its size! It is only 20th the size of Chatgpt but is around 90% as good as Chatgpt. However, please don't rely on WizardLM to provide 100% true information as it might be wrong sometimes.")
+    gr.HTML("This model is awesome for its size! It is only 20th the size of Chatgpt but is around 90% as good as Chatgpt. However, please don't rely on WizardLM to provide 100% true information as it might be wrong sometimes. ")
     chatbot = gr.Chatbot()
     msg = gr.Textbox()
     clear = gr.ClearButton([msg, chatbot])
+    instruction = gr.Textbox(label="Instruction", placeholder=)
     temperature = gr.Slider(minimum=0.1, maximum=1.0, default=0.72, step=0.01, label="Temperature")
     top_p = gr.Slider(minimum=0.1, maximum=1.0, default=0.73, step=0.01, label="Top-p")
     top_k = gr.Slider(minimum=1, maximum=100, default=50, step=1, label="Top-k")
@@ -25,14 +29,18 @@ with gr.Blocks(theme=gr.themes.clean) as demo:
         return gr.update(value="", interactive=True), history + [[user_message, None]]
 
     def bot(history):
+        instruction = history[-1][1] or ""
         user_message = history[-1][0]
+        token1 = llm.tokenize(b"### Instruction: ")
+        token2 = llm.tokenize(instruction.encode())
+        token3 = llm2.tokenize(b"USER: ")
         tokens3 = llm2.tokenize(user_message.encode())
         token4 = llm2.tokenize(b"\n\n### Response:")
         tokens = tokens3 + token4
         history[-1][1] = ""
         count = 0
         output = ""
-        for token in llm2.generate(tokens, top_k=top_k.value, top_p=top_p.value, temp=temperature.value, repeat_penalty=repeat_penalty.value):
+        for token in llm2.generate(tokens, top_k=top_k, top_p=top_p, temp=temperature, repeat_penalty=repeat_penalty):
             text = llm2.detokenize([token])
             output += text.decode()
             count += 1