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import gradio as gr
from huggingface_hub import InferenceClient
import pandas as pd
import torch
"""
For more information on `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")
def respond(
message,
history: list[tuple[str, str]],
max_tokens = 2048,
temperature = 0.7,
top_p = 0.95,
):
messages = [{"role": "system", "content": "You are a moslem bot that always give answer based on quran and hadith!"}]
df = pd.read_csv("moslem-bot-reference.csv")
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": "I want you to answer strictly based on quran and hadith"})
messages.append({"role": "assistant", "content": "I'd be happy to help! Please go ahead and provide the sentence you'd like me to analyze. Please specify whether you're referencing a particular verse or hadith (Prophetic tradition) from the Quran or Hadith, or if you're asking me to analyze a general statement."})
for index, row in df.iterrows():
messages.append({"role": "user", "content": row['user']})
messages.append({"role": "assistant", "content": row['assistant']})
selected_dfs = torch.load('selected_dfs.sav', map_location=torch.device('cpu'))
for df in selected_dfs:
df = df.dropna()
df = df.sample(df.shape[0].div(10))
for index, row in df.iterrows():
messages.append({"role": "user", "content": row['Column1.question']})
messages.append({"role": "assistant", "content": row['Column1.answer']})
messages.append({"role": "user", "content": message})
print(messages)
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
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Slider(minimum=1, maximum=2048, value=2048, 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)",
),
],
examples=[
["Why is men created?"],
["How is life after death?"],
["Please tell me about superstition!"],
["How moses defeat pharaoh?"],
["Please tell me about inheritance law in Islam!"],
["A woman not wear hijab"],
["Worshipping God beside Allah"],
["Blindly obey a person"],
["Make profit from lending money to a friend"],
],
)
if __name__ == "__main__":
demo.launch() |