tarek / app.py
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Update app.py
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import gradio as gr
from huggingface_hub import InferenceClient
from djezzy import load_data,mot_cle,pip,vector_db
"""
For more information on f `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
tableau_de_mots=mot_cle("mots_clés.txt")
mots_a_verifier = tableau_de_mots
docs_text, docs_embeddings = load_data()
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
prompt=pip(message,docs_text, docs_embeddings,mots_a_verifier,vector_db)
messages.append({"role": "user", "content": prompt})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
repo=respond
print(repo)
"""
For information on how to customize the ChatInterface, EB455F peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
#question={"role": "user", "content": message}
#prompt=pip(question,docs_text, docs_embeddings,mots_a_verifier,vector_db)
#print(prompt)
custom_css = """
.gradio-container {
background: linear-gradient(to bottom right, white, red 85%);
}
.gradio-title {
color: #EF4040;
}
"""
logo_path = "djezzy-logo-A1B6F6E26F-seeklogo.com.png"
demo = gr.ChatInterface(
respond,
title="Djezzy Bot",
css=custom_css,
textbox=gr.Textbox(placeholder="What would you like to know about Dezzy?", container=False, scale=7),
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.",placeholder="What would you like to know about Djezzy "),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch(share=True)