Rename app (1).py to app.py

app (1).py → app.py

@@ -6,7 +6,7 @@ from langchain_community.llms import HuggingFaceEndpoint
 from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
 import gradio as gr
 import subprocess
-
+#Using Mistral Modal
 # Ensure Playwright installs required browsers and dependencies
 subprocess.run(["playwright", "install"])
 #subprocess.run(["playwright", "install-deps"])
@@ -20,7 +20,7 @@ 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
 )
-
+#Calling Sentence Transformer
 embedder_model_instance = HuggingFaceInferenceAPIEmbeddings(
     api_key=HUGGINGFACEHUB_API_TOKEN, model_name="sentence-transformers/all-MiniLM-l6-v2"
 )
@@ -29,7 +29,7 @@ graph_config = {
     "llm": {"model_instance": llm_model_instance},
     "embeddings": {"model_instance": embedder_model_instance}
 }
-
+#To Scrape the data and summarize it
 def scrape_and_summarize(prompt, source):
     smart_scraper_graph = SmartScraperGraph(
         prompt=prompt,
@@ -40,24 +40,24 @@ def scrape_and_summarize(prompt, source):
     exec_info = smart_scraper_graph.get_execution_info()
     return result, prettify_exec_info(exec_info)
 
-# Gradio interface
+# Gradio User 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!""")
-
+#(https://github.com/VinciGit00/Scrapegraph-ai) is suggested by the tutor
     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")
+            model_dropdown = gr.Textbox(label="Model", value="Mistral-7B-Instruct-v0.2, As all-MiniLM-l6-v2")
+            prompt_input = gr.Textbox(label="Prompt", value="List me all the doctors name and their timing")
+            source_input = gr.Textbox(label="Source URL", value="https://www.yelp.com/search?find_desc=dentist&find_loc=San+Francisco%2C+CA")
+            scrape_button = gr.Button("Scrape the data")
         
         with gr.Column():
             result_output = gr.JSON(label="Result")
-            exec_info_output = gr.Textbox(label="Execution Info")
+            exec_info_output = gr.Textbox(label="Output Info")
 
     scrape_button.click(
         scrape_and_summarize,