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import streamlit as st
from datasets import load_dataset
load_dataset("juanberasategui/Crypto_Tweets")
load_dataset("Ghosthash/Tweets")
# from transformers import AutoModelForCausalLM, AutoTokenizer
# model_path = "cognitivecomputations/dolphin-2.8-mistral-7b-v02"
# tokenizer = AutoTokenizer.from_pretrained(model_path)
# model = AutoModelForCausalLM.from_pretrained(
# model_path,
# device_map="auto",
# torch_dtype='auto'
# ).eval()
# text = st.text_input("enter text here")
# if text:
# messages = [
# {"role": "user", "content": text},
# ]
# input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
# output_ids = model.generate(input_ids.to('cuda'))
# response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# print(response)
# st.json({
# "response": response
# })
from transformers import pipeline
pipe = pipeline("text-generation", model="cognitivecomputations/dolphin-2.8-mistral-7b-v02", device=1)
text = st.text_input("enter text here")
if text:
response = pipe(text, max_new_tokens=1000)
st.json(response)
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