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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) | |