Running locally

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV
+HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

Gradio_UI.py

@@ -141,9 +141,10 @@ def stream_to_gradio(
 
     for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):
         # Track tokens if model provides them
-        if hasattr(agent.model, "last_input_token_count"):
+        if hasattr(agent.model, "last_input_token_count") and agent.model.last_input_token_count is not None:
             total_input_tokens += agent.model.last_input_token_count
-            total_output_tokens += agent.model.last_output_token_count
+            if agent.model.last_output_token_count is not None:
+                total_output_tokens += agent.model.last_output_token_count
             if isinstance(step_log, ActionStep):
                 step_log.input_token_count = agent.model.last_input_token_count
                 step_log.output_token_count = agent.model.last_output_token_count

app.py

@@ -1,4 +1,27 @@
-from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
+# =====================================================================
+# Local AI Agent using smolagents with LiteLLM and Ollama
+# =====================================================================
+# This application creates a local AI agent that can:
+# - Search the web using DuckDuckGo
+# - Generate images using Hugging Face's text-to-image model
+# - Get current time in different timezones
+# - Check if a number is prime
+# - And more!
+#
+# IMPORTANT: This agent requires local execution with Ollama running on port 11434
+# Remote models from Hugging Face (like Qwen2.5-Coder or Mistral-7B) are often overloaded
+# and may return 'Payment Required' errors or be paused by their providers.
+#
+# Setup requirements:
+# 1. Install Ollama (https://ollama.ai/)
+# 2. Pull the llama3.2 model: `ollama pull llama3.2`
+# 3. Ensure Ollama is running before starting this application
+# 4. Install Python dependencies from requirements.txt
+# =====================================================================
+
+from smolagents import CodeAgent, DuckDuckGoSearchTool, load_tool, tool
+from smolagents.models import LiteLLMModel
+import os
 import datetime
 import requests
 import pytz
@@ -7,7 +30,14 @@ from tools.final_answer import FinalAnswerTool
 
 from Gradio_UI import GradioUI
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
+# =====================================================================
+# Tool Definitions
+# =====================================================================
+# Each tool is defined with the @tool decorator and includes docstrings
+# that help the agent understand when and how to use each tool.
+# =====================================================================
+
+# Simple example tool that can be customized
 @tool
 def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
     #Keep this format for the description / args / args description but feel free to modify the tool
@@ -34,22 +64,31 @@ def get_current_time_in_timezone(timezone: str) -> str:
         return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
 
-# Import tool from Hub
+# Import image generation tool from Hugging Face Hub
+# This allows the agent to create images based on text prompts
 image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
 
 @tool
-def generate_image(prompt:str) -> list:
+def generate_image(prompt:str) -> str:
     """Generate image(s) from a text prompt via HF text-to-image.
     Args:
         prompt: description of the image
     """
-    return image_generation_tool(prompt)    
+    return image_generation_tool(prompt)
 
 @tool
 def duckduckgo_search(query: str, max_results: int = 5) -> str:
-    """Search DuckDuckGo for a query and return the top N results."""
+    """Search DuckDuckGo for a query and return the top N results.
+    
+    Args:
+        query: The search query string to look up on DuckDuckGo
+        max_results: Maximum number of search results to return (default is 5)
+    
+    Returns:
+        A string containing the search results
+    """
     searcher = DuckDuckGoSearchTool(max_results=max_results)
-    return searcher(query) 
+    return searcher(query)
 
 
 @tool
@@ -82,36 +121,64 @@ def is_prime(number: int) -> bool:
 
 
 
-final_answer = FinalAnswerTool()
+# =====================================================================
+# Agent Configuration
+# =====================================================================
 
-# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
+# The FinalAnswerTool is used to provide final responses to the user
+final_answer = FinalAnswerTool()
 
-model = HfApiModel(
-max_tokens=2096,
-temperature=0.5,
-model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-custom_role_conversions=None,
+# IMPORTANT: Remote models are often overloaded or require payment
+# Previous attempts to use these models resulted in errors:
+# - Qwen/Qwen2.5-Coder-32B-Instruct: "Payment Required" error
+# - mistralai/Mistral-7B-Instruct-v0.2: Model was paused
+#
+# Alternative HF endpoint (if needed): 
+# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
+#
+# Instead, we use a local Ollama model which is more reliable:
+
+# Configure LiteLLM to use local Ollama instance
+os.environ["LITELLM_OLLAMA_API_BASE"] = "http://localhost:11434"
+
+# Initialize the model with appropriate parameters
+model = LiteLLMModel(
+    model_name="ollama/llama3.2",  # Using the locally available Llama3.2 model
+    max_tokens=1024,                 # Maximum tokens in the response
+    temperature=0.7,                 # Controls randomness (higher = more creative)
+    model_id="ollama/llama3.2"      # Explicitly set model_id to avoid default to Claude
 )
 
 
 
 
-with open("prompts.yaml", 'r') as stream:
-    prompt_templates = yaml.safe_load(stream)
-    
+# =====================================================================
+# Agent Initialization
+# =====================================================================
+# Initialize the agent with the configured model and tools
 agent = CodeAgent(
     model=model,
-    tools=[final_answer, duckduckgo_search, get_current_time_in_timezone, my_custom_tool, generate_image,
-          is_prime], ## add your tools here (don't remove final answer)
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
+    # List of tools available to the agent - add new tools here
+    tools=[
+        final_answer,               # Required for providing final answers
+        duckduckgo_search,          # Web search capability
+        get_current_time_in_timezone, # Time-related functions
+        my_custom_tool,             # Example custom tool
+        generate_image,             # Image generation
+        is_prime                    # Prime number checker
+    ],
+    max_steps=6,                    # Maximum reasoning steps per query
+    verbosity_level=1,              # Controls logging detail
+    grammar=None,                   # Optional grammar constraints
+    planning_interval=None,         # How often to plan next steps
     name=None,
-    description=None,
-    prompt_templates=prompt_templates
+    description=None
+    # Not specifying prompt_templates will use the default ones from smolagents
 )
 
-
+# =====================================================================
+# Launch the Gradio Web Interface
+# =====================================================================
+# This creates a user-friendly web interface for interacting with the agent
+# Accessible at http://127.0.0.1:7860 by default
 GradioUI(agent).launch()