use google search tool

.env.example

@@ -1,2 +1,4 @@
 SPACE_ID=
-HF_TOKEN=
+HF_TOKEN=
+OPENAI_API_KEY=
+SERPAPI_API_KEY=

app.py

@@ -7,8 +7,8 @@ from huggingface_hub import login
 from dotenv import load_dotenv
 
 import agent
-from app_agents.manager_agent import manager_agent
-# import app_agents.manager_agent as manager_agent
+# from app_agents.manager_agent import manager_agent
+import app_agents.manager_agent as manager_agent
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -143,12 +143,14 @@ def test_init_agent_for_chat(text_input, history):
     #     print(f"Error instantiating agent: {e}")
     #     return f"Error initializing agent: {e}", None
 
-    manager_agent.visualize()
+    # manager_agent.visualize()
 
-    manager_agent.run(text_input)
+    submitted_answer = manager_agent.manager_agent.run(text_input)
+    # print(type(submitted_answer))
+    # print(submitted_answer)
     # submitted_answer = basicAgent(text_input)
 
-    # return submitted_answer
+    return submitted_answer
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:

app_agents/manager_agent copy.py

@@ -0,0 +1,58 @@
+import os
+from smolagents.utils import encode_image_base64, make_image_url
+from smolagents import OpenAIServerModel, CodeAgent, InferenceClientModel
+
+import app_agents.web_agent as web_agent
+from app_agents.web_agent import web_agent
+import app_tools.tools as agent_tools
+
+def check_reasoning_and_plot(final_answer, agent_memory):
+    multimodal_model = OpenAIServerModel("gpt-4o", max_tokens=8096)
+    filepath = "saved_map.png"
+    assert os.path.exists(filepath), "Make sure to save the plot under saved_map.png!"
+    image = Image.open(filepath)
+    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, a plot should be made using px.scatter_map and not any other method (scatter_map looks nicer)."
+    )
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": prompt,
+                },
+                {
+                    "type": "image_url",
+                    "image_url": {"url": make_image_url(encode_image_base64(image))},
+                },
+            ],
+        }
+    ]
+    output = multimodal_model(messages).content
+    print("Feedback: ", output)
+    if "FAIL" in output:
+        raise Exception(output)
+    return True
+
+manager_agent = CodeAgent(
+    model=InferenceClientModel("deepseek-ai/DeepSeek-R1", provider="together", max_tokens=8096),
+    tools=[agent_tools.calculate_cargo_travel_time],
+    managed_agents=[web_agent],
+    additional_authorized_imports=[
+        "geopandas",
+        "plotly",
+        "shapely",
+        "json",
+        "pandas",
+        "numpy",
+    ],
+    planning_interval=5,
+    verbosity_level=2,
+    final_answer_checks=[check_reasoning_and_plot],
+    max_steps=5,
+)

app_agents/manager_agent.py

@@ -2,57 +2,50 @@ import os
 from smolagents.utils import encode_image_base64, make_image_url
 from smolagents import OpenAIServerModel, CodeAgent, InferenceClientModel
 
+# from gradio_tools import (StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool,
+#                           TextToVideoTool)
+# from langchain.agents import initialize_agent
+# from langchain.memory import ConversationBufferMemory
+
 import app_agents.web_agent as web_agent
-from app_agents.web_agent import web_agent
-import app_tools.tools as agent_tools
+# import app_tools.tools as agent_tools
 
-def check_reasoning_and_plot(final_answer, agent_memory):
-    multimodal_model = OpenAIServerModel("gpt-4o", max_tokens=8096)
-    filepath = "saved_map.png"
-    assert os.path.exists(filepath), "Make sure to save the plot under saved_map.png!"
-    image = Image.open(filepath)
-    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, a plot should be made using px.scatter_map and not any other method (scatter_map looks nicer)."
-    )
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": prompt,
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {"url": make_image_url(encode_image_base64(image))},
-                },
-            ],
-        }
-    ]
-    output = multimodal_model(messages).content
-    print("Feedback: ", output)
-    if "FAIL" in output:
-        raise Exception(output)
-    return True
+# tools = [StableDiffusionTool().langchain, ImageCaptioningTool().langchain,
+#          StableDiffusionPromptGeneratorTool().langchain, TextToVideoTool().langchain]
+# memory = ConversationBufferMemory(memory_key="chat_history")
 
 manager_agent = CodeAgent(
     model=InferenceClientModel("deepseek-ai/DeepSeek-R1", provider="together", max_tokens=8096),
-    tools=[agent_tools.calculate_cargo_travel_time],
-    managed_agents=[web_agent],
+    tools=[],
+    planning_interval=4,
+    verbosity_level=2,
+    managed_agents=[web_agent.web_agent],
+    max_steps=10,
+    # grammar=DEFAULT_CODEAGENT_REGEX_GRAMMAR,
     additional_authorized_imports=[
-        "geopandas",
-        "plotly",
-        "shapely",
-        "json",
+        "requests",
+        "zipfile",
+        "os",
         "pandas",
         "numpy",
+        "sympy",
+        "json",
+        "bs4",
+        "pubchempy",
+        "xml",
+        "yahoo_finance",
+        "Bio",
+        "sklearn",
+        "scipy",
+        "pydub",
+        "io",
+        "PIL",
+        "chess",
+        "PyPDF2",
+        "pptx",
+        "torch",
+        "datetime",
+        "csv",
+        "fractions",
     ],
-    planning_interval=5,
-    verbosity_level=2,
-    final_answer_checks=[check_reasoning_and_plot],
-    max_steps=15,
 )

app_agents/web_agent.py

@@ -1,13 +1,39 @@
-from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel, VisitWebpageTool
+# from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel, VisitWebpageTool, ManagedAgent
 
+# model = InferenceClientModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct", provider="together")
+
+# web_agent = CodeAgent(
+#     model=model,
+#     tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
+#     additional_authorized_imports=["pandas"],
+#     name="web_agent",
+#     description="Browses the web to find information",
+#     verbosity_level=0,
+#     max_steps=20,
+# )
+
+from dotenv import load_dotenv
+from smolagents import  InferenceClientModel, CodeAgent, DuckDuckGoSearchTool, GoogleSearchTool, VisitWebpageTool
+
+load_dotenv()
 model = InferenceClientModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct", provider="together")
 
 web_agent = CodeAgent(
     model=model,
-    tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
-    additional_authorized_imports=["pandas"],
+    tools=[
+        # DuckDuckGoSearchTool(),
+        GoogleSearchTool("serper"),
+        VisitWebpageTool()
+    ],
     name="web_agent",
-    description="Browses the web to find information",
+    description="Runs web searches for you. Give it your query as an argument.",
     verbosity_level=0,
-    max_steps=20,
-)
+    max_steps=10,
+)
+
+# managed_web_agent = CodeAgent(
+#     model=model,
+#     agent=web_agent,
+#     name="web_search",
+#     description="Runs web searches for you. Give it your query as an argument."
+# )

requirements.txt

@@ -8,4 +8,8 @@ kaleido
 smolagents
 typing
 duckduckgo-search
-huggingface_hub
+huggingface_hub
+markdownify
+transformers
+gradio_tools
+langchain