sailfish commited on
Commit
386aaeb
1 Parent(s): b50b796
Files changed (1) hide show
  1. app.py +0 -68
app.py CHANGED
@@ -11,20 +11,6 @@ huggingface_token = os.getenv("SECRET_ENV_VARIABLE")
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  #client = InferenceClient(api_key=huggingface_token)
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  client = InferenceClient(model=model_name, token=huggingface_token)
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- '''
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- import requests
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-
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- API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-1B"
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- headers = {"Authorization": "Bearer "}
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-
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- def query(payload):
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- response = requests.post(API_URL, headers=headers, json=payload)
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- return response.json()
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-
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- output = query({
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- "inputs": "Can you please let us know more details about your ",
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- })
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- '''
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  def generate_text(
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  prompt,
@@ -53,60 +39,6 @@ def generate_text(
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- def respond(
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- message,
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- history: list[tuple[str, str]],
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- system_message,
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- max_tokens,
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- temperature,
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- top_p,
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- ):
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- messages = [{"role": "system", "content": system_message}]
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-
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- for val in history:
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- if val[0]:
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- messages.append({"role": "user", "content": val[0]})
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- if val[1]:
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- messages.append({"role": "assistant", "content": val[1]})
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-
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- messages.append({"role": "user", "content": message})
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-
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- response = ""
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-
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- for message in client.chat_completion(
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- messages,
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- max_tokens=max_tokens,
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- stream=True,
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- temperature=temperature,
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- top_p=top_p,
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- ):
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- token = message.choices[0].delta.content
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-
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- response += token
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- yield response
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-
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-
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- """
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- For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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-
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- demo = gr.ChatInterface(
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- #respond,
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- generate_text,
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- additional_inputs=[
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- gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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- gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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- gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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- gr.Slider(
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- minimum=0.1,
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- maximum=1.0,
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- value=0.95,
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- step=0.05,
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- label="Top-p (nucleus sampling)",
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- ),
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- ],
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- )
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- """
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-
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  with gr.Blocks() as demo:
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  gr.Markdown("Q&A App")
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  #client = InferenceClient(api_key=huggingface_token)
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  client = InferenceClient(model=model_name, token=huggingface_token)
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  def generate_text(
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  prompt,
 
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  with gr.Blocks() as demo:
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  gr.Markdown("Q&A App")
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