First commit - code refactoring

README.md

@@ -5,7 +5,7 @@ colorFrom: indigo
 colorTo: indigo
 sdk: gradio
 sdk_version: 5.25.2
-app_file: app.py
+app_file: main.py
 pinned: false
 hf_oauth: true
 # optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.

app.py

@@ -1,196 +0,0 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-
-# (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:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    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
-
-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
-
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    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)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        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(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
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            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})
-        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}"})
-
-    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}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        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(
-        """
-        **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).
-        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.
-        """
-    )
-
-    gr.LoginButton()
-
-    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(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-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
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    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")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)

requirements.txt

@@ -1,2 +1,6 @@
-gradio
-requests
+gradio~=5.26.0
+gradio[oauth]
+requests~=2.32.3
+smolagents~=1.13.0
+python-dotenv~=1.1.0
+pandas~=2.2.3

src/agent/base_agent.py

@@ -0,0 +1,9 @@
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+
+    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

src/core/evaluator.py

@@ -0,0 +1,50 @@
+import os
+import pandas as pd
+
+from rest_clients.hs_evaluator_client import HsEvaluatorClient
+from src.agent.base_agent import BasicAgent
+
+
+class Evaluator:
+
+    def __init__(self, profile):
+        self.profile = profile
+        self.username = profile.username if profile else None
+        self.space_id = os.getenv("SPACE_ID")
+        self.agent = BasicAgent()
+        self.hs_evaluator_client: HsEvaluatorClient | None = None
+
+    def run_and_submit(self):
+        if not self.username:
+            return "Please Login to Hugging Face with the button.", None
+
+        questions = self.get_hs_evaluator_client().fetch_questions()
+        if not questions:
+            return "Fetched questions list is empty or invalid format.", None
+
+        results_log, answers_payload = self._run_agent(questions)
+        if not answers_payload:
+            return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+        return self.get_hs_evaluator_client().submit_answers(answers_payload, results_log)
+
+    def _run_agent(self, questions):
+        results_log = []
+        answers_payload = []
+        for item in questions:
+            task_id = item.get("task_id")
+            question_text = item.get("question")
+            if not task_id or question_text is None:
+                continue
+            try:
+                submitted_answer = self.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})
+            except Exception as e:
+                results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+        return results_log, answers_payload
+
+    def get_hs_evaluator_client(self):
+        if not self.hs_evaluator_client:
+            self.hs_evaluator_client = HsEvaluatorClient(self.username, self.space_id)
+        return self.hs_evaluator_client

src/main.py

@@ -0,0 +1,5 @@
+from src.ui.App import App
+
+if __name__ == "__main__":
+    app = App()
+    app.run()

src/rest_clients/hs_evaluator_client.py

@@ -0,0 +1,41 @@
+import pandas as pd
+import requests
+
+
+class HsEvaluatorClient:
+
+    def __init__(self, username, space_id):
+        self.space_id = space_id
+        self.username = username
+        self.base_url = "https://agents-course-unit4-scoring.hf.space"
+
+    def fetch_questions(self):
+        try:
+            response = requests.get(f"{self.base_url}/questions", timeout=15)
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print(f"Error fetching questions: {e}")
+            return None
+
+    def submit_answers(self, answers_payload, results_log):
+        agent_code = f"https://huggingface.co/spaces/{self.space_id}/tree/main"
+        submission_data = {
+            "username": self.username.strip(),
+            "agent_code": agent_code,
+            "answers": answers_payload
+        }
+        try:
+            response = requests.post(f"{self.base_url}/submit", json=submission_data, timeout=60)
+            response.raise_for_status()
+            result_data = response.json()
+            final_status = (
+                f"Submission Successful!\n"
+                f"User: {result_data.get('username')}\n"
+                f"Overall Score: {result_data.get('score', 'N/A')}% "
+                f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+                f"Message: {result_data.get('message', 'No message received.')}"
+            )
+            return final_status, pd.DataFrame(results_log)
+        except Exception as e:
+            return f"Submission Failed: {e}", pd.DataFrame(results_log)

src/rest_clients/open_weather_client.py

@@ -0,0 +1,31 @@
+import os
+import requests
+from dotenv import load_dotenv
+
+class OpenWeatherClient:
+
+    def __init__(self):
+        load_dotenv()
+        self.api_key = os.getenv("OPEN_WEATHER_TOKEN")
+        self.base_url = "http://api.openweathermap.org/data/2.5/weather"
+
+    def get_weather(self, location: str):
+        params = {
+            "q": location,
+            "appid": self.api_key,
+            "units": "metric"
+        }
+        try:
+            response = requests.get(self.base_url, params=params)
+            response.raise_for_status()
+            weather_data = response.json()
+
+            condition = weather_data["weather"][0]["description"]
+            temp_c = weather_data["main"]["temp"]
+            return {
+                "location": location,
+                "condition": condition.capitalize(),
+                "temperature": f"{temp_c}°C"
+            }
+        except requests.exceptions.RequestException as e:
+            return {"error": f"Failed to fetch weather data: {e}"}

src/tools/external_tools.py

@@ -0,0 +1,6 @@
+from smolagents import DuckDuckGoSearchTool
+
+
+duck_duck_go_search_tool = DuckDuckGoSearchTool()
+
+

src/tools/weater_info_tool.py

@@ -0,0 +1,22 @@
+from smolagents import Tool
+
+from rest_clients.open_weather_client import OpenWeatherClient
+
+
+class WeatherInfoTool(Tool):
+    name = "weather_info"
+    description = "Fetches real weather information for a given location using OpenWeatherMap."
+    inputs = {
+        "location": {
+            "type": "string",
+            "description": "The location to get weather information for."
+        }
+    }
+    output_type = "string"
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.weather_client = OpenWeatherClient()
+
+    def forward(self, location: str):
+        return self.weather_client.get_weather(location)

src/ui/App.py

@@ -0,0 +1,30 @@
+import gradio as gr
+from src.core.evaluator import Evaluator
+
+class App:
+    def __init__(self):
+        self.interface = gr.Blocks()
+        self._build_interface()
+
+    def _build_interface(self):
+        with self.interface:
+            gr.Markdown("# Basic Agent Evaluation Runner")
+            gr.Markdown("Follow instructions to run and evaluate the agent.")
+            gr.LoginButton()
+            run_button = gr.Button("Run Evaluation & Submit All Answers")
+            status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+            results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+            run_button.click(
+                fn=App.evaluate_agent,
+                outputs=[status_output, results_table]
+            )
+
+    @staticmethod
+    def evaluate_agent(profile: gr.OAuthProfile | None):
+        evaluator = Evaluator(profile)
+        return evaluator.run_and_submit()
+
+    def run(self):
+        print("Launching Gradio Interface...")
+        self.interface.launch(debug=True, share=False)