Create app.py

app.py

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+"""
+Dog Weight Calculator Agent - Strands Agents Version
+
+This is a rewrite of the original Hugging Face OpenAI-based ReAct agent
+using Amazon's Strands Agents SDK. The original implementation used a
+manual ReAct loop with regex parsing. Strands handles all of this
+automatically through its model-driven approach.
+
+Original: Manual ReAct loop with OpenAI GPT-4
+New: Strands Agents with native tool calling
+"""
+
+import os
+import gradio as gr
+from strands import Agent, tool
+from strands.models.openai import OpenAIModel
+
+# =============================================================================
+# TOOLS
+# =============================================================================
+# In Strands, tools are simply Python functions decorated with @tool.
+# The framework automatically extracts the function signature, docstring,
+# and type hints to create tool specifications for the LLM.
+
+@tool
+def calculate(expression: str) -> str:
+    """
+    Evaluate a mathematical expression and return the result.
+    
+    Args:
+        expression: A mathematical expression to evaluate (e.g., "4 * 7 / 3", "37 + 20")
+    
+    Returns:
+        The result of the calculation as a string
+    """
+    try:
+        # Using eval for simple math - in production, consider using a safer parser
+        result = eval(expression)
+        return str(result)
+    except Exception as e:
+        return f"Error evaluating expression: {e}"
+
+
+@tool
+def average_dog_weight(breed: str) -> str:
+    """
+    Get the average weight of a dog breed.
+    
+    Args:
+        breed: The name of the dog breed (e.g., "Border Collie", "Scottish Terrier", "Toy Poodle")
+    
+    Returns:
+        A string describing the average weight of the specified breed
+    """
+    # Normalize the breed name for matching
+    breed_lower = breed.lower()
+    
+    if "scottish terrier" in breed_lower:
+        return "Scottish Terriers average 20 lbs"
+    elif "border collie" in breed_lower:
+        return "A Border Collie's average weight is 37 lbs"
+    elif "toy poodle" in breed_lower:
+        return "A Toy Poodle's average weight is 7 lbs"
+    elif "bulldog" in breed_lower:
+        return "A Bulldog weighs 51 lbs"
+    elif "labrador" in breed_lower:
+        return "A Labrador Retriever's average weight is 65 lbs"
+    elif "german shepherd" in breed_lower:
+        return "A German Shepherd's average weight is 75 lbs"
+    elif "golden retriever" in breed_lower:
+        return "A Golden Retriever's average weight is 65 lbs"
+    elif "beagle" in breed_lower:
+        return "A Beagle's average weight is 25 lbs"
+    elif "chihuahua" in breed_lower:
+        return "A Chihuahua's average weight is 5 lbs"
+    elif "great dane" in breed_lower:
+        return "A Great Dane's average weight is 140 lbs"
+    else:
+        return f"I don't have specific data for {breed}. An average dog weighs about 50 lbs"
+
+
+# =============================================================================
+# SYSTEM PROMPT
+# =============================================================================
+# With Strands, we don't need to specify the ReAct format in the prompt.
+# The framework handles tool selection and execution automatically.
+# We just describe the agent's purpose and behavior.
+
+SYSTEM_PROMPT = """
+You are a helpful assistant that specializes in answering questions about dog weights.
+You have access to tools that can:
+1. Look up the average weight of specific dog breeds
+2. Perform mathematical calculations
+
+When a user asks about dog weights:
+- Use the average_dog_weight tool to look up breed-specific information
+- If they ask about multiple dogs, look up each breed separately
+- Use the calculate tool for any math (like adding weights together)
+
+Always provide clear, helpful answers about dog weights.
+""".strip()
+
+
+# =============================================================================
+# AGENT SETUP
+# =============================================================================
+
+def create_agent():
+    """
+    Create and configure the Strands agent.
+    
+    The agent can use either:
+    - OpenAI models (requires OPENAI_API_KEY)
+    - Amazon Bedrock models (requires AWS credentials, default)
+    """
+    # Check for OpenAI API key
+    openai_api_key = os.environ.get('OPENAI_API_KEY')
+    
+    if openai_api_key:
+        # Use OpenAI if API key is available
+        model = OpenAIModel(
+            client_args={"api_key": openai_api_key},
+            model_id="gpt-4o",
+            params={
+                "temperature": 0,
+                "max_tokens": 1024
+            }
+        )
+        print("Using OpenAI GPT-4o model")
+    else:
+        # Fall back to Bedrock (default in Strands)
+        # Requires AWS credentials to be configured
+        model = None  # Strands uses Bedrock by default
+        print("Using Amazon Bedrock (default)")
+    
+    # Create the agent with our tools
+    if model:
+        agent = Agent(
+            model=model,
+            system_prompt=SYSTEM_PROMPT,
+            tools=[calculate, average_dog_weight]
+        )
+    else:
+        agent = Agent(
+            system_prompt=SYSTEM_PROMPT,
+            tools=[calculate, average_dog_weight]
+        )
+    
+    return agent
+
+
+def query(question: str) -> str:
+    """
+    Process a question using the Strands agent.
+    
+    Unlike the original implementation that required manual loop management
+    and regex parsing, Strands handles all of this automatically:
+    - Tool selection based on the question
+    - Tool execution
+    - Multi-step reasoning
+    - Response generation
+    
+    Args:
+        question: The user's question about dog weights
+        
+    Returns:
+        The agent's response
+    """
+    try:
+        # Create a fresh agent for each query
+        agent = create_agent()
+        
+        # Invoke the agent - Strands handles the entire agentic loop
+        result = agent(question)
+        
+        # Extract the final response
+        # The result object contains the full conversation and metrics
+        return str(result)
+        
+    except Exception as e:
+        return f"Error processing question: {str(e)}"
+
+
+# =============================================================================
+# GRADIO INTERFACE
+# =============================================================================
+
+def process_question(question: str) -> str:
+    """Wrapper function for Gradio interface."""
+    return query(question)
+
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=process_question,
+    inputs=gr.Textbox(
+        label="Enter your question",
+        placeholder="e.g., I have 2 dogs, a border collie and a scottish terrier. What is their combined weight?",
+        lines=3
+    ),
+    outputs=gr.Textbox(label="Answer", lines=5),
+    title="🐕 Dog Weight Calculator (Strands Agents)",
+    description="""
+    Ask about dog weights or perform calculations!
+    
+    **Examples:**
+    - How much does a toy poodle weigh?
+    - I have 2 dogs, a border collie and a scottish terrier. What is their combined weight?
+    - What's heavier, a Great Dane or a German Shepherd?
+    
+    *Powered by Amazon Strands Agents SDK*
+    """,
+    examples=[
+        ["How much does a toy poodle weigh?"],
+        ["I have 2 dogs, a border collie and a scottish terrier. What is their combined weight?"],
+        ["What's the average weight of a Labrador Retriever?"],
+        ["If I have a Chihuahua and a Great Dane, how much do they weigh together?"],
+    ],
+    theme=gr.themes.Soft()
+)
+
+
+# =============================================================================
+# DEMO / TESTING
+# =============================================================================
+
+def run_demo():
+    """Run some demo queries to test the agent."""
+    print("\n" + "="*60)
+    print("STRANDS AGENTS - DOG WEIGHT CALCULATOR DEMO")
+    print("="*60 + "\n")
+    
+    test_questions = [
+        "How much does a toy poodle weigh?",
+        "I have 2 dogs, a border collie and a scottish terrier. What is their combined weight?",
+    ]
+    
+    for question in test_questions:
+        print(f"Question: {question}")
+        print("-" * 40)
+        answer = query(question)
+        print(f"Answer: {answer}")
+        print("\n")
+
+
+if __name__ == "__main__":
+    import sys
+    
+    if "--demo" in sys.argv:
+        # Run demo mode
+        run_demo()
+    else:
+        # Launch Gradio interface
+        iface.launch()