Update app.py

app.py

@@ -3,32 +3,89 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from langchain.agents import AgentExecutor, create_react_agent
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import PromptTemplate
+from langchain_community.tools import DuckDuckGoSearchRun # <-- IMPORTED DUCKDUCKGO
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+# --- Tool Definition ---
+# Replaced Google Search with DuckDuckGo. This tool doesn't require an API key.
+search_tool = DuckDuckGoSearchRun()
+
+
+# --- Agent Definition ---
+class RealAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        # Initialize the language model
+        self.llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest", temperature=0)
+        
+        # Define the prompt template
+        prompt_template = """
+        Answer the following questions as best you can. You have access to the following tools:
+
+        {tools}
+
+        Use the following format:
+
+        Question: the input question you must answer
+        Thought: you should always think about what to do
+        Action: the action to take, should be one of [{tool_names}]
+        Action Input: the input to the action
+        Observation: the result of the action
+        ... (this Thought/Action/Action Input/Observation can repeat N times)
+        Thought: I now know the final answer
+        Final Answer: the final answer to the original input question
+
+        Begin!
+
+        Question: {input}
+        Thought:{agent_scratchpad}
+        """
+        self.prompt = PromptTemplate.from_template(prompt_template)
+        
+        # Define the tools - now using the DuckDuckGo tool
+        self.tools = [search_tool]
+        
+        # Create the agent
+        self.agent = create_react_agent(
+            self.llm,
+            self.tools,
+            self.prompt,
+        )
+        
+        # Create the agent executor
+        self.agent_executor = AgentExecutor(
+            agent=self.agent,
+            tools=self.tools,
+            verbose=True,
+            handle_parsing_errors=True,
+            max_iterations=10,
+        )
+
     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
+        try:
+            # We must set the tool name explicitly in the invoke call for some versions of langchain
+            response = self.agent_executor.invoke({
+                "input": question,
+                "tool_names": ", ".join([t.name for t in self.tools])
+                })
+            return response.get("output", "No answer found.")
+        except Exception as e:
+            return f"Error invoking agent: {e}"
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    Fetches all questions, runs the RealAgent on them, submits all answers,
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -38,13 +95,13 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate Agent
     try:
-        agent = BasicAgent()
+        agent = RealAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
@@ -139,22 +196,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# Real Agent Evaluation Runner (with DuckDuckGo)")
     gr.Markdown(
         """
         **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        1.  This space is a solution to the final assignment of the Hugging Face AI Agents course, using Gemini Pro and DuckDuckGo.
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
         """
     )
 
@@ -163,7 +213,6 @@ with gr.Blocks() as demo:
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -173,9 +222,8 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+    space_id_startup = os.getenv("SPACE_ID")
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -183,7 +231,7 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    if space_id_startup:
         print(f"✅ SPACE_ID found: {space_id_startup}")
         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
@@ -192,5 +240,5 @@ if __name__ == "__main__":
 
     print("-"*(60 + len(" App Starting ")) + "\n")
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    print("Launching Gradio Interface for Real Agent Evaluation...")
     demo.launch(debug=True, share=False)