Update app.py

app.py

@@ -1,7 +1,5 @@
-import os
 import gradio as gr
-from transformers import Tool
-from transformers.agents import (
+from transformers import (
     ReactCodeAgent,
     ReactJsonAgent,
     HfApiEngine,
@@ -9,66 +7,41 @@ from transformers.agents import (
     stream_to_gradio,
 )
 from transformers.agents.search import DuckDuckGoSearchTool
-import requests
-from markdownify import markdownify as md
-from requests.exceptions import RequestException
-import re
-import spaces
+from visit_webpage_tool import VisitWebpageTool  # Import the VisitWebpageTool class
 from huggingface_hub import login
+import os
+import time
+import random
 
-# Read the Hugging Face API token from the environment variable
-hf_token = os.getenv("HF_TOKEN")
+# Define the model
+model = "meta-llama/Meta-Llama-3.1-70B-Instruct"
 
-# Authenticate with the Hugging Face API
-login(token=hf_token)
+# Initialize the LLM engine with the Hugging Face token
+hf_token = os.getenv("HF_TOKEN")
+if not hf_token:
+    raise ValueError("Hugging Face API token not found. Please set the hf_token environment variable.")
 
-class VisitWebpageTool(Tool):
-    """
-    A tool to visit a webpage and return its content as a markdown string.
-    """
-    name = "visit_webpage"
-    description = "Visits a webpage at the given URL and returns its content as a markdown string."
-    inputs = {
-        "url": {
-            "type": "text",
-            "description": "The URL of the webpage to visit.",
-        }
-    }
-    output_type = "text"
+# Authenticate with Hugging Face
+login(hf_token)
 
-    def forward(self, url: str) -> str:
-        """
-        Fetch the webpage content and convert it to markdown.
-        """
-        try:
-            response = requests.get(url)
-            response.raise_for_status()
-            markdown_content = md(response.text).strip()
-            markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)
-            return markdown_content
-        except RequestException as e:
-            return f"Error fetching the webpage: {str(e)}"
-        except Exception as e:
-            return f"An unexpected error occurred: {str(e)}"
+# Initialize the LLM engine with reduced max_new_tokens
+llm_engine = HfApiEngine(model)
 
-# Initialize the LLM engine with the Hugging Face API token
-llm_engine = HfApiEngine(model="meta-llama/Meta-Llama-3.1-70B-Instruct")
-
-# Initialize the web agent with necessary tools and engine
+# Create the web agent
 web_agent = ReactJsonAgent(
     tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
     llm_engine=llm_engine,
     max_iterations=10,
 )
 
-# Create a managed web agent
+# Wrap the web agent into a ManagedAgent
 managed_web_agent = ManagedAgent(
     agent=web_agent,
     name="search_agent",
     description="Runs web searches for you. Give it your query as an argument.",
 )
 
-# Initialize the manager agent with the managed web agent
+# Create the manager agent
 manager_agent = ReactCodeAgent(
     tools=[],
     llm_engine=llm_engine,
@@ -76,11 +49,21 @@ manager_agent = ReactCodeAgent(
     additional_authorized_imports=["time", "datetime"],
 )
 
+# Define a retry mechanism with exponential backoff
+def retry_with_backoff(func, retries=5, backoff_factor=2):
+    for attempt in range(retries):
+        try:
+            return func()
+        except Exception as e:
+            if attempt < retries - 1:
+                sleep_time = backoff_factor ** attempt + random.uniform(0, 1)
+                time.sleep(sleep_time)
+            else:
+                raise e
+
+# Define the Gradio interaction function
 @spaces.GPU(duration=120)
 def interact_with_agent(task):
-    """
-    Interact with the agent and stream the responses to Gradio.
-    """
     messages = []
     messages.append(gr.ChatMessage(role="user", content=task))
     yield messages
@@ -96,7 +79,8 @@ with gr.Blocks() as demo:
     gr.Markdown("# multi-agent-web-browser")
     gr.Markdown("Gradio space based on the multiagent_web_assistant cookbook https://huggingface.co/learn/cookbook/multiagent_web_assistant")
     text_input = gr.Textbox(lines=1, label="Chat Message", value="How many years ago was Stripe founded?")
-    submit = gr.Button("Run multi-agent system!")
+    text_input = gr.Textbox(lines=1, label="Chat Message", value="How many years ago was Stripe founded?")
+    submit = gr.Button("Run web search agent!")
     chatbot = gr.Chatbot(
         label="Agent",
         type="messages",