Upload agent.py

agent.py

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+from transformers import Tool, HfAgent
+from huggingface_hub import list_models
+import requests
+from typing import Optional, List
+import random
+
+# First, let's define some custom tools the agent can use
+
+class WebSearchTool(Tool):
+    name = "web_search"
+    description = ("A tool that performs a web search using a search engine API. "
+                   "Input should be a search query. Output will be search results.")
+    
+    inputs = ["text"]
+    outputs = ["text"]
+    
+    def __call__(self, query: str):
+        # In a real implementation, you would call a search API here
+        # For demonstration, we'll return mock results
+        return f"Search results for '{query}': 1. Relevant result 1, 2. Relevant result 2"
+
+class CalculatorTool(Tool):
+    name = "calculator"
+    description = ("A tool for performing mathematical calculations. "
+                   "Input should be a mathematical expression. Output will be the result.")
+    
+    inputs = ["text"]
+    outputs = ["text"]
+    
+    def __call__(self, expression: str):
+        try:
+            result = eval(expression)  # Note: In production, use a safer eval method
+            return str(result)
+        except:
+            return "Error: Could not evaluate the expression"
+
+class CurrentTimeTool(Tool):
+    name = "get_current_time"
+    description = ("A tool that returns the current time in UTC. "
+                   "No input needed. Output will be the current time.")
+    
+    inputs = []
+    outputs = ["text"]
+    
+    def __call__(self):
+        from datetime import datetime
+        return datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC")
+
+class WikipediaTool(Tool):
+    name = "wikipedia_search"
+    description = ("A tool that searches Wikipedia. "
+                   "Input should be a search term. Output will be a summary from Wikipedia.")
+    
+    inputs = ["text"]
+    outputs = ["text"]
+    
+    def __call__(self, term: str):
+        # Mock implementation - in real use, you'd call the Wikipedia API
+        return f"Wikipedia summary for '{term}': This is a summary about {term}."
+
+# Now let's create the agent with these tools and some pre-trained tools
+
+def create_agent():
+    # Load pre-trained tools from the Hub
+    agent = HfAgent(
+        "https://api-inference.huggingface.co/models/bigcode/starcoder",
+        additional_tools=[
+            WebSearchTool(),
+            CalculatorTool(),
+            CurrentTimeTool(),
+            WikipediaTool()
+        ],
+        # These parameters help with performance
+        max_new_tokens=200,
+        temperature=0.7,
+        top_p=0.9,
+    )
+    return agent
+
+# Example usage of the agent
+if __name__ == "__main__":
+    agent = create_agent()
+    
+    # Test the agent with some sample queries
+    queries = [
+        "What's the capital of France?",
+        "Calculate 123 * 45",
+        "What time is it now?",
+        "Tell me about Albert Einstein",
+        "Search for the latest news about AI"
+    ]
+    
+    for query in queries:
+        print(f"Query: {query}")
+        result = agent.run(query)
+        print(f"Result: {result}\n")
+    
+    # To evaluate on the benchmark, you would use:
+    # from transformers.benchmarks import evaluate_agent
+    # benchmark_score = evaluate_agent(agent)
+    # print(f"Benchmark score: {benchmark_score}")