import streamlit as st

from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
from scrapegraphai.graphs import SmartScraperGraph
from scrapegraphai.utils import prettify_exec_info

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
modelNomic = AutoModel.from_pretrained("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)

graph_config = {
   "llm": {
      "model-instance": model,
      "temperature": 1,
      "format": "json",  # Ollama needs the format to be specified explicitly
      "model_tokens": 4096, #  depending on the model set context length
   },
   "embeddings": {
      "model-instance": modelNomic,
      "temperature": 0,
   }
}

# ************************************************
# Create the SmartScraperGraph instance and run it
# ************************************************

smart_scraper_graph = SmartScraperGraph(
   prompt="List me shoes in first page with names, prices and image urls",
   # also accepts a string with the already downloaded HTML code
   source="https://www.footlocker.co.uk/en/category/sale/men.html",
   config=graph_config
)

result = smart_scraper_graph.run()
print(result)

x = st.slider('Select a value')
st.write(x, 'squared is', x * x)