10% accurate, first working version

.gitignore

@@ -1 +1,2 @@
 .env 
+__pycache__/

app.py

@@ -1,10 +1,18 @@
+import re
 import os
 import gradio as gr
 import requests
 import inspect
+import datetime
+from textwrap import dedent
 import pandas as pd
 from dotenv import load_dotenv
 
+# Import smolagents components
+from smolagents import CodeAgent, HfApiModel, DuckDuckGoSearchTool
+
+
+
 # Load environment variables from .env file
 load_dotenv()
 print(os.getenv("HF_TOKEN"))
@@ -17,14 +25,120 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+# Initialize the search tool
+search_tool = DuckDuckGoSearchTool()
+
+
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
+        # Create a filename with current date and time
+        current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M")
+        self.filename = f"questions_{current_time}.txt"
+        print(f"Questions will be written to {self.filename}")
+
+        # Clear the file if it exists or create a new one
+        with open(self.filename, 'w', encoding='utf-8') as f:
+            f.write('')  # Create empty file
+
+
+        # Initialize the Large Language Model
+        # The model is used by both agents in this simple setup
+        self.model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct")
+
+
+        # Define the Web Search Agent
+        # This agent is specialised for searching the web using a specific tool
+        self.web_search_agent = CodeAgent(
+            model=self.model,  # Assign the model to the agent [
+            tools=[DuckDuckGoSearchTool()],  # Provide the web search tool
+            name="web_search_agent",  # Give the agent a name
+            # Describe its capability [
+            description="Searches the web for information.",
+            verbosity_level=1,  # Set verbosity level for logging
+            max_steps=5,  # Limit the steps the agent can take
+        )
+
+        # Define the Manager Agent
+        # This agent manages tasks and delegates to other agents
+        self.manager_agent = CodeAgent(
+            model=self.model,  # Assign the model to the manager
+            tools=[],
+            managed_agents=[self.web_search_agent],  # Specify the agents this manager oversees
+            name="manager_agent",  # Give the manager agent a name
+            description="Manages tasks by delegating to other agents.",  # Describe its role
+            additional_authorized_imports=[
+                "json", "re", "pandas", "numpy", "math", "collections", "itertools", "stat", "statistics", "queue", "unicodedata", "time", "random", "datetime"],  # Allow specific imports
+            verbosity_level=1,  # Set verbosity level
+            max_steps=5,  # Limit the steps
+        )
+
+        print("MultiAgentSystem initialization complete.")
+
+
     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}")
-        return fixed_answer
+
+
+        # For all other questions, use the manager agent with web search
+        manager_prompt = dedent(f"""
+            I need to answer the following question accurately:
+
+            {question}
+
+            Please analyze this question and determine the best approach to answer it.
+            If needed, use web search to find relevant information.
+            Provide a concise, accurate answer to the question.
+        """)
+
+        manager_agent_response = "I apologize, but I couldn't find an answer to this question."
+        answer = ""
+        source = ""
+        try:
+            manager_agent_response = self.manager_agent.run(manager_prompt)
+            source = "manager_agent"
+            
+            # # Clean up the answer - remove explanations, etc.
+            # if len(manager_agent_response) > 300:
+            #     # Try to extract just the answer
+            #     answer_pattern = r'(?:answer|result)(?:\s+is)?(?:\s*:)?\s*(.+?)(?:\.|$)'
+            #     answer_match = re.search(answer_pattern, manager_agent_response, re.IGNORECASE)
+                
+            #     if answer_match:
+            #         answer = answer_match.group(1).strip()
+            #     else:
+            #         # Get the last paragraph
+            #         paragraphs = [p for p in answer.split('\n') if p.strip()]
+            #         if paragraphs:
+            #             answer = paragraphs[-1].strip()
+            #     source = "long_answer"
+            
+            #return answer
+            
+        except Exception as e:
+            print(f"Error in manager agent: {e}")
+            source = f"Exception {e} "
+
+            # # Fall back to direct web search
+            # try:
+            #     answer = self.web_search_agent.run(f"Please find accurate information to answer: {question}")
+            #     source = "web_search_agent"
+            
+            # except Exception as e2:
+            #     print(f"Error in web agent: {e2}")
+            #     answer="I apologize, but I couldn't find an answer to this question."
+
+
+        # Append the question to the file
+        with open(self.filename, 'a', encoding='utf-8') as f:
+            f.write(f"{question}\n")
+            f.write(f"ANSWER by {source}: {manager_agent_response}\n")
+            f.write(f"{'*'*50}\n")
+
+        print(f"Final answer: {manager_agent_response}")
+        return manager_agent_response
+    
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

poetry.lock

@@ -586,6 +586,75 @@ packaging = "*"
 typing-extensions = ">=4.0,<5.0"
 websockets = ">=10.0,<16.0"
 
+[[package]]
+name = "greenlet"
+version = "3.2.1"
+description = "Lightweight in-process concurrent programming"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+    {file = "greenlet-3.2.1-cp39-cp39-win32.whl", hash = "sha256:b7503d6b8bbdac6bbacf5a8c094f18eab7553481a1830975799042f26c9e101b"},
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+    {file = "greenlet-3.2.1.tar.gz", hash = "sha256:9f4dd4b4946b14bb3bf038f81e1d2e535b7d94f1b2a59fdba1293cd9c1a0a4d7"},
+]
+
+[package.extras]
+docs = ["Sphinx", "furo"]
+test = ["objgraph", "psutil"]
+
 [[package]]
 name = "groovy"
 version = "0.1.2"
@@ -1349,6 +1418,28 @@ tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "ole
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
+[[package]]
+name = "playwright"
+version = "1.52.0"
+description = "A high-level API to automate web browsers"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "playwright-1.52.0-py3-none-macosx_10_13_x86_64.whl", hash = "sha256:19b2cb9d4794062008a635a99bd135b03ebb782d460f96534a91cb583f549512"},
+    {file = "playwright-1.52.0-py3-none-macosx_11_0_arm64.whl", hash = "sha256:0797c0479cbdc99607412a3c486a3a2ec9ddc77ac461259fd2878c975bcbb94a"},
+    {file = "playwright-1.52.0-py3-none-macosx_11_0_universal2.whl", hash = "sha256:7223960b7dd7ddeec1ba378c302d1d09733b8dac438f492e9854c85d3ca7144f"},
+    {file = "playwright-1.52.0-py3-none-manylinux1_x86_64.whl", hash = "sha256:d010124d24a321e0489a8c0d38a3971a7ca7656becea7656c9376bfea7f916d4"},
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+    {file = "playwright-1.52.0-py3-none-win32.whl", hash = "sha256:cd0bdf92df99db6237a99f828e80a6a50db6180ef8d5352fc9495df2c92f9971"},
+    {file = "playwright-1.52.0-py3-none-win_amd64.whl", hash = "sha256:dcbf75101eba3066b7521c6519de58721ea44379eb17a0dafa94f9f1b17f59e4"},
+    {file = "playwright-1.52.0-py3-none-win_arm64.whl", hash = "sha256:9d0085b8de513de5fb50669f8e6677f0252ef95a9a1d2d23ccee9638e71e65cb"},
+]
+
+[package.dependencies]
+greenlet = ">=3.1.1,<4.0.0"
+pyee = ">=13,<14"
+
 [[package]]
 name = "primp"
 version = "0.15.0"
@@ -1530,6 +1621,24 @@ files = [
     {file = "pydub-0.25.1.tar.gz", hash = "sha256:980a33ce9949cab2a569606b65674d748ecbca4f0796887fd6f46173a7b0d30f"},
 ]
 
+[[package]]
+name = "pyee"
+version = "13.0.0"
+description = "A rough port of Node.js's EventEmitter to Python with a few tricks of its own"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "pyee-13.0.0-py3-none-any.whl", hash = "sha256:48195a3cddb3b1515ce0695ed76036b5ccc2ef3a9f963ff9f77aec0139845498"},
+    {file = "pyee-13.0.0.tar.gz", hash = "sha256:b391e3c5a434d1f5118a25615001dbc8f669cf410ab67d04c4d4e07c55481c37"},
+]
+
+[package.dependencies]
+typing-extensions = "*"
+
+[package.extras]
+dev = ["black", "build", "flake8", "flake8-black", "isort", "jupyter-console", "mkdocs", "mkdocs-include-markdown-plugin", "mkdocstrings[python]", "mypy", "pytest", "pytest-asyncio", "pytest-trio", "sphinx", "toml", "tox", "trio", "trio", "trio-typing", "twine", "twisted", "validate-pyproject[all]"]
+
 [[package]]
 name = "pygments"
 version = "2.19.1"
@@ -2087,4 +2196,4 @@ files = [
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.12"
-content-hash = "6b90481ab0325b1613e3268ba5fefc7aa4fa1ff6a6df8444050505f594d372c8"
+content-hash = "0636f2e99ebcd2665712f585e4d3afc15d928f57ba17262419eb411badc1f4b1"

pyproject.toml

@@ -12,7 +12,8 @@ dependencies = [
     "gradio[oauth] (>=5.27.0,<6.0.0)",
     "requests (>=2.32.3,<3.0.0)",
     "pandas (>=2.2.3,<3.0.0)",
-    "python-dotenv (>=1.1.0,<2.0.0)"
+    "python-dotenv (>=1.1.0,<2.0.0)",
+    "playwright (>=1.52.0,<2.0.0)"
 ]
 
 

test_agents,py

@@ -0,0 +1,42 @@
+# test_agents,py
+import os
+from dotenv import load_dotenv
+
+# Import the BasicAgent from your app module
+try:
+    from app import BasicAgent
+except ImportError as e:
+    print(f"Error importing BasicAgent from app.py: {e}")
+    print("Please ensure app.py is in the same directory or accessible in the Python path.")
+    exit(1)
+
+# --- Define Question-Answer Pairs ---
+# Note: The 'A' part is just for reference here; the agent will generate its own answer.
+QA_PAIRS = {
+    "What is the capital of France?": "Paris",
+    "Who wrote 'Hamlet'?": "William Shakespeare",
+    "What is the formula for water?": "H2O",
+    "How does photosynthesis work?": "Plants use sunlight, water, and carbon dioxide to create their own food.",
+    # Agent should find current data
+    "What is the current population of Earth?": "Approximately 8 billion",
+}
+
+
+def run_test_questions():
+    """Instantiates the agent and runs it on the predefined questions."""
+    print("--- Starting Agent Test ---")
+    # Load environment variables (needed for BasicAgent initialization)
+    load_dotenv()
+    print(f"HF_TOKEN found: {'Yes' if os.getenv('HF_TOKEN') else 'No'}")
+
+    agent = BasicAgent()
+
+    for question in QA_PAIRS.keys():
+        print(f"\n--- Testing Question ---")
+        print(f"Q: {question}")
+        answer = agent(question)  # Call the agent instance
+        print(f"Agent A: {answer}")
+
+
+if __name__ == "__main__":
+    run_test_questions()