Update agent.py

agent.py

@@ -1,5 +1,3 @@
-# agent.py
-
 import os
 import time
 import hashlib
@@ -57,7 +55,7 @@ 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}")
@@ -71,14 +69,6 @@ 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
@@ -161,8 +151,59 @@ def generate_rss_feed(storage_location, url):
     else:
         return "No data available."
 
-# Function to handle user input and generate response
-def chat_interface(message, history, system_message, max_tokens, temperature, top_p, storage_location, urls, scrape_interval, content_type, selector):
-    response = respond(message, history, system_message, max_tokens, temperature, top_p)
-    history.append((message, response))
-    return history, response
+# 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
+
+# Define the Gradio interface
+def create_interface():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                message = gr.Textbox(label="Message")
+                system_message = gr.Textbox(value="You are a helpful assistant.", label="System message")
+                max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+                temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+                top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+                storage_location = gr.Textbox(value="scraped_data", label="Storage Location")
+                urls = gr.Textbox(label="URLs (comma separated)")
+                scrape_interval = gr.Slider(minimum=1, maximum=60, value=5, step=1, label="Scrape Interval (minutes)")
+                content_type = gr.Radio(choices=["text", "media", "both"], value="text", label="Content Type")
+                start_button = gr.Button("Start Scraping")
+                stop_button = gr.Button("Stop Scraping")
+                csv_output = gr.Textbox(label="CSV Output", interactive=False)
+
+            with gr.Column():
+                chat_history = gr.Chatbot(label="Chat History")
+                response_box = gr.Textbox(label="Response")
+
+        stop_scraping_flag = [False]
+        start_button.click(start_scraping, inputs=[storage_location, urls, scrape_interval, content_type], outputs=csv_output)
+        stop_button.click(stop_scraping, inputs=[stop_scraping_flag], outputs=[csv_output])
+        message.submit(respond, inputs=[message, chat_history, system_message, max_tokens, temperature, top_p], outputs=[chat_history, response_box])
+
+        # Add a button to display the CSV content for a selected URL
+        with gr.Row():
+            selected_url = gr.Textbox(label="Select URL for CSV Content")
+            csv_button = gr.Button("Display CSV Content")
+            csv_output = gr.Textbox(label="CSV Content Output", interactive=False)
+
+        csv_button.click(display_csv, inputs=[storage_location, selected_url], outputs=csv_output)
+
+        # Add a button to display the RSS feed for a selected URL
+        with gr.Row():
+            selected_url = gr.Textbox(label="Select URL for RSS Feed")
+            rss_button = gr.Button("Generate RSS Feed")
+            rss_output = gr.Textbox(label="RSS Feed Output", interactive=False)
+
+        rss_button.click(generate_rss_feed, inputs=[storage_location, selected_url], outputs=rss_output)
+
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()