Update app.py

app.py

@@ -3,7 +3,7 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from smolagents import Tool, tool, OpenAIServerModel, DuckDuckGoSearchTool, CodeAgent, VisitWebpageTool
+from smolagents import FinalAnswerTool, Tool, tool, OpenAIServerModel, DuckDuckGoSearchTool, CodeAgent, VisitWebpageTool
 
 
 from dotenv import load_dotenv
@@ -18,14 +18,95 @@ OPENAI_TOKEN = os.getenv("OPENAPI_KEY")
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+class SlpMultiAgent:
     def __init__(self):
         print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
         fixed_answer = "This is a default answer."
         print(f"Agent returning fixed answer: {fixed_answer}")
+        
+# Increase max_tokens to allow for complete responses
+        model = OpenAIServerModel(
+            model_id="gpt-3.5-turbo", 
+            temperature=0.0, 
+            max_tokens=1500
+        )
+        # Here you can implement your agent logic, tools, and model calls
+        web_agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
+            model=model,
+            additional_authorized_imports=["pandas"],
+            max_steps=10,
+            name="WebAgent",
+            verbosity_level=0,
+            description="An agent that can search the web, visit webpages, and calculate cargo travel times between locations."
+        )
+
+        manager_agent = CodeAgent(
+                model=OpenAIServerModel("gpt-3.5-turbo"),
+                tools=[],
+                managed_agents=[web_agent],
+                name="ManagerAgent",
+                description="A manager agent that can delegate tasks to other agents and manage their execution.",
+                additional_authorized_imports=[
+                    "pandas",
+                ],
+                planning_interval=5,
+                verbosity_level=2,
+                max_steps=15,
+                final_answer_checks=[check_reasoning]
+                )
+        manager_agent.run("""
+        You are a question answering agent. That specializes in complex questions that require multiple steps to answer.
+        Take a few steps and think about the question before answering.
+        You can use the tools available to you, but you should not use them unless necessary.
+        You should always try to answer the question using your own knowledge and reasoning.
+        If you need to use a tool, you should explain why you are using it and what you expect to find.
+        If you are not sure about something, you should say so and explain why you are not sure.
+        You should always try to provide a complete and accurate answer to the question.
+        If you are not able to answer the question, you should say so and explain why
+
+        Never try to process strings using code: when you have a string to read, just print it and you'll see it.
+                        
+        Here is the question: {question}
+        Thoughts: [your reasoning about how to solve the problem]
+        Code:
+        ```py
+        # Your Python code here
+        ```<end_code>
+
+        The code block MUST start with ```py on its own line and end with ```<end_code> on its own line.
+        """)
         return fixed_answer
+    
+def check_reasoning(final_answer, agent_memory):
+    multimodal_model = OpenAIServerModel("gpt-4", 
+    max_tokens=1500)
+    prompt = (
+        f"Here is a user-given task and the agent steps: {agent_memory.get_succinct_steps()}. Now here is the plot that was made."
+        "Please check that the reasoning process and plot are correct: do they correctly answer the given task?"
+        "First list reasons why yes/no, then write your final decision: PASS in caps lock if it is satisfactory, FAIL if it is not."
+        "Don't be harsh: if the plot mostly solves the task, it should pass."
+        "To pass the question should be answered correctly and the reasoning should be sound."
+        "The final answer is: {final_answer}. "
+    )
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": prompt,
+                }
+            ],
+        }
+    ]
+    output = multimodal_model(messages).content
+    print("Reasoning and plot check output:", output)
+    if "fail" in output.lower():
+        print("Reasoning check failed. Please review the agent's reasoning.")
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -48,7 +129,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = SlpMultiAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None