Update app.py

app.py

@@ -1 +1,120 @@
-ss
+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
+import json
+import re
+
+# Ensure Playwright installs required browsers and dependencies
+subprocess.run(["playwright", "install"])
+#subprocess.run(["playwright", "install-deps"])
+
+# 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"
+repo_id = "Qwen/Qwen2.5-72B-Instruct"
+
+llm_model_instance = HuggingFaceEndpoint(
+    repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
+)
+
+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,
+        "model_tokens": 100000, 
+    },
+    "embeddings": {"model_instance": embedder_model_instance}
+}
+#######
+def clean_json_string(json_str):
+    """
+    Removes any comments or prefixes before the actual JSON content.
+    Returns the cleaned JSON string.
+    """
+    # Find the first occurrence of '{'
+    json_start = json_str.find('{')
+    if json_start == -1:
+        # If no '{' is found, try with '[' for arrays
+        json_start = json_str.find('[')
+        if json_start == -1:
+            return json_str  # Return original if no JSON markers found
+    
+    # Extract everything from the first JSON marker
+    cleaned_str = json_str[json_start:]
+    
+    # Verify it's valid JSON
+    try:
+        json.loads(cleaned_str)
+        return cleaned_str
+    except json.JSONDecodeError:
+        return json_str  # Return original if cleaning results in invalid JSON
+
+def scrape_and_summarize(prompt, source):
+    smart_scraper_graph = SmartScraperGraph(
+        prompt=prompt,
+        source=source,
+        config=graph_config
+    )
+    result = smart_scraper_graph.run()
+    
+    # Clean the result if it's a string
+    if isinstance(result, str):
+        result = clean_json_string(result)
+    
+    exec_info = smart_scraper_graph.get_execution_info()
+    return result, prettify_exec_info(exec_info)
+
+
+
+#######
+# 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 library [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!
+*Note: You might need to add "Output only the results; do not add any comments or include 'JSON OUTPUT' or similar phrases" in your prompt to ensure the LLM only provides the result.*
+""")
+    with gr.Row():
+        with gr.Column():
+            
+            model_dropdown = gr.Textbox(label="Model", value="Qwen/Qwen2.5-72B-Instruct")
+            prompt_input = gr.Textbox(label="Prompt", value="List all the press releases with their headlines and urls. Output only the results; do not add any comments or include 'JSON OUTPUT' or similar phrases.")
+            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()