summary and sentiment analysis

agent-py-bot/agent.py

@@ -200,23 +200,25 @@ from agents.webagent import run_web_agent, save_data
 
 async def run_web_agent_and_process_result(topic, folder):
     news_data = run_web_agent(topic, folder)
-    # summary = summarize_data(news_data)
+
+    print(f"[{datetime.now()}] Doing summarisation and sentiment analysis with an AI model.")
     
-    # with open(os.path.join(folder, "summary_log.txt"), 'a') as log_file:
-    #     log_file.write(f"{datetime.now()}: {summary}\n")
-    
-    # Process the result immediately after obtaining it
     user_message = f"Summarize these news and make sentiment analysis on each news and one overall: {news_data}"
-    
-    print(f"[{datetime.now()}] Doing sentiment analysis with AI model.")
     start = time.time()
     query_result = await query_llm(user_message, "openhermes")
     print(f"[{datetime.now()}] AI call returned in {time.time() - start} seconds.")
     news_data["summary"] = query_result
+
+    user_message = "do sentiment analysis on theese news and report overall sentiment for the day from 1 to 100. Here's the current news articles: {news_data}"
+    start = time.time()
+    query_result = await query_llm(user_message, "openhermes")
+    print(f"[{datetime.now()}] AI call returned in {time.time() - start} seconds.")
+    news_data["sentimen"] = query_result
+    
     save_data(news_data, folder)
     
     with open(os.path.join(folder, "summary_log.txt"), 'a') as log_file:
-        log_file.write(f"{datetime.now()}: {query_result}\n")
+        log_file.write(f"\n\n\n{datetime.now()}: {query_result}\n")
 
     # Process the query_result as needed
 
@@ -227,7 +229,7 @@ async def async_main():
 
     while True:
         await run_web_agent_and_process_result(topic=topic, folder=folder)
-        await asyncio.sleep(interval * 60)  # Convert hours to seconds
+        await asyncio.sleep(interval * 3600)  # Convert hours to seconds
 
 if __name__ == '__main__':
     asyncio.run(async_main())