Update app.py

app.py

@@ -3,10 +3,15 @@ import requests
 import time
 import random
 import os
+import logging
 from bs4 import BeautifulSoup
 import trafilatura
 from huggingface_hub import InferenceClient
 
+# Set up logging
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
 USER_AGENTS = [
     'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
     'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:89.0) Gecko/20100101 Firefox/89.0',
@@ -99,34 +104,47 @@ def search_searx(query, instance_url='https://searx.org', categories='general',
 
 
 def summarize_with_llm(query, search_results):
-    client = InferenceClient(
-        "mistralai/Mistral-Nemo-Instruct-2407",
-        token=os.getenv("HUGGINGFACE_API_KEY"),
-    )
-    
-    system_prompt = """You are an AI assistant tasked with summarizing search results. Your goal is to provide a concise, informative summary of the search results in relation to the user's query. Focus on the most relevant information and present it in a clear, organized manner."""
-    
-    user_prompt = f"""Query: {query}
+    logger.debug(f"Attempting to summarize results for query: {query}")
+    try:
+        api_key = os.getenv("HUGGINGFACE_API_KEY")
+        if not api_key:
+            logger.error("HUGGINGFACE_API_KEY environment variable is not set")
+            return "Error: Hugging Face API key is not set. Please set the HUGGINGFACE_API_KEY environment variable."
+
+        logger.debug("Initializing InferenceClient")
+        client = InferenceClient(
+            "mistralai/Mistral-Nemo-Instruct-2407",
+            token=api_key,
+        )
+        
+        system_prompt = """You are an AI assistant tasked with summarizing search results. Your goal is to provide a concise, informative summary of the search results in relation to the user's query. Focus on the most relevant information and present it in a clear, organized manner."""
+        
+        user_prompt = f"""Query: {query}
 
 Search Results:
 {search_results}
 
 Please provide a summary of the search results in relation to the query. Highlight the most relevant information, identify any common themes or contradictions, and present the information in a clear and concise manner. If there are any gaps in the information or areas that require further research, please mention them as well."""
 
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": user_prompt}
-    ]
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt}
+        ]
 
-    summary = ""
-    for message in client.chat_completion(
-        messages=messages,
-        max_tokens=500,
-        stream=True,
-    ):
-        summary += message.choices[0].delta.content
+        logger.debug("Sending request to Hugging Face API")
+        summary = ""
+        for message in client.chat_completion(
+            messages=messages,
+            max_tokens=500,
+            stream=True,
+        ):
+            summary += message.choices[0].delta.content
 
-    return summary
+        logger.debug("Successfully generated summary")
+        return summary
+    except Exception as e:
+        logger.exception(f"Error in summarize_with_llm: {str(e)}")
+        return f"Error generating summary: {str(e)}"
 
 def create_gradio_interface():
     with gr.Blocks() as demo:
@@ -202,18 +220,27 @@ def create_gradio_interface():
                 results = gr.Markdown("### Search Results and Summary will appear here...")
         
         def perform_search_and_summarize(q, url, cats, num, use_traf, t_range, lang, safe, engines, sort, chars):
+        logger.debug(f"Performing search for query: {q}")
+        try:
             search_results = search_searx(q, instance_url=url, categories=cats, num_results=int(num), 
                                           use_trafilatura=use_traf, time_range=t_range, language=lang, safesearch=int(safe),
                                           search_engines=engines, sort_by=sort, max_chars=chars)
+            
+            logger.debug("Search completed, attempting to summarize")
             summary = summarize_with_llm(q, search_results)
+            
             return f"## AI-Generated Summary\n\n{summary}\n\n## Original Search Results\n\n{search_results}"
-        
+        except Exception as e:
+            logger.exception(f"Error in perform_search_and_summarize: {str(e)}")
+            return f"An error occurred: {str(e)}"
+
         search_button.click(
             perform_search_and_summarize,
             inputs=[query, instance_url, categories, num_results, use_trafilatura, time_range, language, safesearch,
                     search_engines, sort_by, max_chars],
             outputs=results
         )
+
         
         gr.Markdown(
             """
@@ -229,4 +256,5 @@ def create_gradio_interface():
 iface = create_gradio_interface()
 
 if __name__ == "__main__":
+    logger.info("Starting the application")
     iface.launch()