add scrape graph script

app.py

@@ -1,5 +1,40 @@
 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)
 

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+transformers
+scrapegraphai