Added model

app.py

@@ -1,9 +1,72 @@
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
 import gradio as gr
+import subprocess
 
+# Ensure Playwright installs required browsers and dependencies
+subprocess.run(["playwright", "install"])
+# subprocess.run(["playwright", "install-deps"])
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load environment variables
+load_dotenv()
+HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
 
+# Initialize the model instances
+repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
+llm_model_instance = HuggingFaceEndpoint(
+    repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
+)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+embedder_model_instance = HuggingFaceInferenceAPIEmbeddings(
+    api_key=HUGGINGFACEHUB_API_TOKEN, model_name="sentence-transformers/all-MiniLM-l6-v2"
+)
+
+graph_config = {
+    "llm": {"model_instance": llm_model_instance},
+    "embeddings": {"model_instance": embedder_model_instance}
+}
+
+
+def scrape_and_summarize(prompt, source):
+    smart_scraper_graph = SmartScraperGraph(
+        prompt=prompt,
+        source=source,
+        config=graph_config
+    )
+    result = smart_scraper_graph.run()
+    exec_info = smart_scraper_graph.get_execution_info()
+    return result, prettify_exec_info(exec_info)
+
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Scrape websites, no-code version")
+    gr.Markdown("""Easily scrape and summarize web content using advanced AI models on the Hugging Face Hub without writing any code. Input your desired prompt and source URL to get started.
+                This is a no-code version of the excellent lib [ScrapeGraphAI](https://github.com/VinciGit00/Scrapegraph-ai).
+                It's a basic demo and a work in progress. Please contribute to it to make it more useful!""")
+
+    with gr.Row():
+        with gr.Column():
+            model_dropdown = gr.Textbox(label="Model", value="Mistral-7B-Instruct-v0.2")
+            prompt_input = gr.Textbox(label="Prompt",
+                                      value="List me all the press releases with their headlines and urls.")
+            source_input = gr.Textbox(label="Source URL", value="https://www.whitehouse.gov/")
+            scrape_button = gr.Button("Scrape and Summarize")
+
+        with gr.Column():
+            result_output = gr.JSON(label="Result")
+            exec_info_output = gr.Textbox(label="Execution Info")
+
+    scrape_button.click(
+        scrape_and_summarize,
+        inputs=[prompt_input, source_input],
+        outputs=[result_output, exec_info_output]
+    )
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.launch()