Update app.py
Browse files
app.py
CHANGED
@@ -1,6 +1,7 @@
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import gradio as gr
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from huggingface_hub import InferenceClient
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import random
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# Define the model to be used
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model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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@@ -13,19 +14,28 @@ system_prompt_text = "You are a smart and helpful co-worker of Thailand based mu
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with open("info.md", "r") as file:
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info_md_content = file.read()
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def format_prompt_mixtral(message, history, info_md_content):
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prompt = "<s>"
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if history:
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for user_prompt, bot_response in history:
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prompt += f"[INST] {user_prompt} [/INST]"
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prompt += f" {bot_response}</s> "
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prompt += f"[INST] {
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return prompt
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def chat_inf(prompt, history, seed, temp, tokens, top_p, rep_p):
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# Prepend the system prompt to the user prompt
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full_prompt = f"{system_prompt_text}, {prompt}"
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-
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generate_kwargs = dict(
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temperature=temp,
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max_new_tokens=tokens,
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@@ -35,7 +45,7 @@ def chat_inf(prompt, history, seed, temp, tokens, top_p, rep_p):
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seed=seed,
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)
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formatted_prompt = format_prompt_mixtral(
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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output = ""
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for response in stream:
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@@ -76,8 +86,8 @@ with gr.Blocks(auth=("Admin", "0112358")) as app: # Add auth here
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seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
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tokens = gr.Slider(label="Max new tokens", value=3840, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens")
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temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
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top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum
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rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum
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hid1 = gr.Number(value=1, visible=False)
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import gradio as gr
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from huggingface_hub import InferenceClient
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import random
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import textwrap
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# Define the model to be used
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model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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with open("info.md", "r") as file:
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info_md_content = file.read()
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# Chunk the info.md content into smaller sections
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chunk_size = 2500 # Adjust this size as needed
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info_md_chunks = textwrap.wrap(info_md_content, chunk_size)
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def get_relevant_chunk(prompt, chunks):
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# For simplicity, we just use the first chunk. You can improve this by adding more sophisticated logic.
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return chunks[0]
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def format_prompt_mixtral(message, history, info_md_content):
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prompt = "<s>"
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relevant_chunk = get_relevant_chunk(message, info_md_content)
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prompt += f"{relevant_chunk}\n\n" # Add the relevant chunk of info.md at the beginning
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prompt += f"{system_prompt_text}\n\n" # Add the system prompt
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if history:
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for user_prompt, bot_response in history:
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prompt += f"[INST] {user_prompt} [/INST]"
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prompt += f" {bot_response}</s> "
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prompt += f"[INST] {message} [/INST]"
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return prompt
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def chat_inf(prompt, history, seed, temp, tokens, top_p, rep_p):
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generate_kwargs = dict(
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temperature=temp,
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max_new_tokens=tokens,
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seed=seed,
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)
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formatted_prompt = format_prompt_mixtral(prompt, history, info_md_chunks)
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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output = ""
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for response in stream:
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seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
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tokens = gr.Slider(label="Max new tokens", value=3840, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens")
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temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
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top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
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rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
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hid1 = gr.Number(value=1, visible=False)
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