Update agent.py

agent.py

@@ -1,3 +1,5 @@
+# agent.py
+
 import os
 import time
 import hashlib
@@ -55,7 +57,7 @@ def monitor_urls(storage_location, urls, scrape_interval, content_type, selector
                             current_content = driver.page_source
 
                         current_hash = hashlib.md5(str(current_content).encode('utf-8')).hexdigest()
-                        if current_hash != previous_hashes[url]:
+                        if current_hash!= previous_hashes[url]:
                             previous_hashes[url] = current_hash
                             date_time_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                             HISTORY.append(f"Change detected at {url} on {date_time_str}")
@@ -69,6 +71,14 @@ def monitor_urls(storage_location, urls, scrape_interval, content_type, selector
     except Exception as e:
         logging.error(f"Error starting ChromeDriver: {e}")
 
+# Function to define the chat response function using the Mistral model
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    model = AutoModelForSeq2SeqLM.from_pretrained_model("mistralai/Mixtral-8x7B-Instruct-v0.1")
+    tokenizer = AutoTokenizer.from_pretrained_model("mistralai/Mixtral-8x7B-Instruct-v0.1")
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    response = pipe(f"User: {message}\nHistory: {history}\nSystem: {system_message}", max_length=max_tokens, temperature=temperature, top_p=top_p)[0]
+    return response
+
 # Function to start scraping
 def start_scraping(storage_location, urls, scrape_interval, content_type, selector=None):
     global CURRENT_TASK, HISTORY
@@ -127,14 +137,6 @@ def display_csv(storage_location, url):
     else:
         return "No data available."
 
-# Define the chat response function using the Mistral model
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    model = AutoModelForSeq2SeqLM.from_pretrained_model("mistralai/Mixtral-8x7B-Instruct-v0.1")
-    tokenizer = AutoTokenizer.from_pretrained_model("mistralai/Mixtral-8x7B-Instruct-v0.1")
-    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
-    response = pipe(f"User: {message}\nHistory: {history}\nSystem: {system_message}", max_length=max_tokens, temperature=temperature, top_p=top_p)[0]
-    return response
-
 # Function to generate RSS feed for a given URL
 def generate_rss_feed(storage_location, url):
     hostname = urlparse(url).hostname