Update app.py

app.py

@@ -1,146 +1,44 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
-from dotenv import load_dotenv
-from openai import OpenAI
-from tenacity import retry, stop_after_attempt, wait_exponential
-
-# Load environment variables
-load_dotenv()
-
+from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-OPENAI_MODEL = "openai/gpt-4.1"  # or "gpt-3.5-turbo" based on your preference
 
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        """Initialize the agent with OpenAI client and setup."""
-        print("BasicAgent initializing...")
-        self.client = OpenAI(
-            api_key=os.environ["API_KEY"],
-            base_url="https://models.github.ai/inference",
+        print("BasicAgent initialized.")
+        # Initialize the model
+        #model = HfApiModel()
+        model = OpenAIServerModel(model_id="openai/gpt-4.1",api_key=os.environ["API_KEY"],api_base="https://models.github.ai/inference")
+        # Initialize the search tool
+        search_tool = DuckDuckGoSearchTool()
+        # Initialize Agent
+        self.agent = CodeAgent(
+            model = model,
+            tools=[search_tool]
         )
-        print("BasicAgent initialized successfully.")
-
-    @retry(
-        stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10)
-    )
-    def _get_completion(self, prompt: str) -> str:
-        """Get completion from OpenAI with retry logic."""
-        try:
-            response = self.client.chat.completions.create(
-                model=OPENAI_MODEL,
-                messages=[
-                    {
-                        "role": "developer",
-                        "content": """
-                        You are an expert research assistant that provides precise, accurate answers. Before responding, use this hidden planning phase (which will not be shown to users):
-                        
-                        ```
-                        <planning>
-                        1. Classify the question type:
-                           - Arithmetic/mathematical calculation
-                           - Factual lookup (dates, codes, definitions)
-                           - Complex knowledge (requires synthesis of multiple facts)
-                           - Subjective/opinion-based (requires reasoning with caveats)
-                        
-                        2. For each type:
-                           - Arithmetic: Calculate step-by-step to ensure accuracy
-                           - Factual lookup: Identify the specific data point needed
-                           - Complex knowledge: Break down into key components and relationships
-                           - Subjective: Note major perspectives and evidence for each
-                        
-                        3. Check for potential ambiguities or misinterpretations
-                        4. Formulate the most precise answer possible
-                        </planning>
-                        ```
-                        
-                        ## Response Format
-                        
-                        After your planning, provide your answer in this format:
-                        
-                        **Answer:** [Your concise, precise answer]
-                        
-                        For factual questions, include only the exact information requested - no extra text.
-                        For complex questions, provide a concise, well-structured response focused on accuracy.
-                        
-                        ## Examples
-                        
-                        **Q: What is 493 × 27?**
-                        <planning>Arithmetic calculation: 493 × 27 = (493 × 20) + (493 × 7) = 9,860 + 3,451 = 13,311</planning>
-                        **Answer:** 13,311
-                        
-                        **Q: Which country has the smallest land area in South America?**
-                        <planning>Factual lookup: South American countries by land area. Smallest is Suriname at 63,251 square miles.</planning>
-                        **Answer:** Suriname
-                        
-                        **Q: How does atmospheric carbon dioxide affect ocean acidity?**
-                        <planning>Complex knowledge question requiring synthesis of chemistry concepts...</planning>
-                        **Answer:** Atmospheric CO₂ dissolves in seawater forming carbonic acid (H₂CO₃), which releases hydrogen ions and lowers pH. This process, called ocean acidification, has increased ocean acidity by approximately 30% since the Industrial Revolution.""",
-                    },
-                    {"role": "user", "content": prompt},
-                ],
-                temperature=0.5,  # Lower temperature for more consistent outputs
-                # max_tokens=1000,
-            )
-            return response.choices[0].message.content.strip()
-        except Exception as e:
-            print(f"Error in OpenAI API call: {e}")
-            raise
-
-    def _preprocess_question(self, question: str) -> str:
-        """Preprocess the question to enhance clarity and context."""
-        enhanced_prompt = f"""Please analyze and answer the following question from the GAIA benchmark.
-        Question: {question}
-
-        Provide a clear, specific answer that can be evaluated through exact matching.
-        If the question requires multiple steps, please show your reasoning but ensure the final answer is clearly stated.
-        """
-        return enhanced_prompt
-
     def __call__(self, question: str) -> str:
-        """Process the question and return an answer."""
         print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer =self.agent.run(question)
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
 
-        try:
-            # Preprocess the question
-            enhanced_prompt = self._preprocess_question(question)
-
-            # Get completion from OpenAI
-            response = self._get_completion(enhanced_prompt)
-
-            # Extract the final answer
-            # If the response contains multiple lines or explanations,
-            # we'll try to extract just the final answer
-            answer_lines = response.strip().split("\n")
-            final_answer = answer_lines[-1].strip()
-
-            # Log the response for debugging
-            print(f"Agent generated answer: {final_answer[:100]}...")
-
-            return final_answer
-
-        except Exception as e:
-            print(f"Error processing question: {e}")
-            return f"Error: {str(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,
     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
+    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.")
@@ -167,16 +65,16 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-            print("Fetched questions list is empty.")
-            return "Fetched questions list is empty or invalid format.", None
+             print("Fetched questions list is empty.")
+             return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
     except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
     except requests.exceptions.JSONDecodeError as e:
-        print(f"Error decoding JSON response from questions endpoint: {e}")
-        print(f"Response text: {response.text[:500]}")
-        return f"Error decoding server response for questions: {e}", None
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for questions: {e}", None
     except Exception as e:
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
@@ -193,36 +91,18 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             continue
         try:
             submitted_answer = agent(question_text)
-            answers_payload.append(
-                {"task_id": task_id, "submitted_answer": submitted_answer}
-            )
-            results_log.append(
-                {
-                    "Task ID": task_id,
-                    "Question": question_text,
-                    "Submitted Answer": submitted_answer,
-                }
-            )
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:
-            print(f"Error running agent on task {task_id}: {e}")
-            results_log.append(
-                {
-                    "Task ID": task_id,
-                    "Question": question_text,
-                    "Submitted Answer": f"AGENT ERROR: {e}",
-                }
-            )
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload,
-    }
+    # 4. Prepare Submission 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
@@ -276,11 +156,9 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
-
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         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).
@@ -292,19 +170,20 @@ 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
-    )
+    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(fn=run_and_submit_all, outputs=[status_output, results_table])
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
+    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") # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -312,18 +191,14 @@ 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 repo URLs if SPACE_ID is found
         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"
-        )
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
     else:
-        print(
-            "ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."
-        )
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
 
-    print("-" * (60 + len(" App Starting ")) + "\n")
+    print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)