doc: update readme

README.md

@@ -27,6 +27,7 @@ DuckDuckGo and GoogleSearch have too many rate limit,
 This code assume that a local running instance of searxng is on http://localhost:8888
 
 On windows run it with:
+```
 $PORT=8888
 docker run --rm `
  -p ${PORT}:8080 `
@@ -34,13 +35,23 @@ docker run --rm `
  -e "BASE_URL=http://localhost:$PORT/" `
  -e "INSTANCE_NAME=my-instance" `
  -d searxng/searxng
-
+```
  be sure to allow the json format in /etc/seraxng/settings.yml
 
+ ```
+  # remove format to deny access, use lower case.
+  # formats: [html, csv, json, rss]
+  formats:
+    - html
+    - json
+```
+
 
-For the same reason, for my tool for text to speech I locally run a speech to text docker image running whisper.cpp
+For the same reason, for my tool for text to speech I locally run a speech to text docker image running whisper.cpp  
 whith ffmpeg installed for mp3 > wav conversion
 
+It is launched by ttools/sst.py
+
 And if I really have no longer any available token on openai or gemini, I can run a VLLM instance.
 
 ## Instrumentation
@@ -48,8 +59,8 @@ And if I really have no longer any available token on openai or gemini, I can ru
 Instrumentation is enabled, 
 an done locally with (Arize-ai phoenix)[https://github.com/Arize-ai/phoenix]
 a server is launched with: 
-
+```
 python -m phoenix.server.main serve
-
+```
 and can be consulted on: http://127.0.0.1:6006
 

app-withshortcut.py

@@ -0,0 +1,234 @@
+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+from dotenv import load_dotenv
+
+from myagent import BasicAgent  # Import your agent class from myagent.py
+from multiagents import MultiAgent
+
+from phoenix.otel import register
+from openinference.instrumentation.smolagents import SmolagentsInstrumentor
+
+# use space_host var to determine if running in HF space or locally, if so register local instrumentation
+space_host_startup = os.getenv("SPACE_HOST")
+if not space_host_startup:
+    register()
+    SmolagentsInstrumentor().instrument()
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+load_dotenv()
+
+max_questions = 20
+
+# known answer, already solved, to avoid computation cost
+known_answers = {
+    "f918266a-b3e0-4914-865d-4faa564f1aef": "0",
+    "a1e91b78-d3d8-4675-bb8d-62741b4b68a6": "3",
+    "2d83110e-a098-4ebb-9987-066c06fa42d0": "right",
+    "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": "3",
+    "9d191bce-651d-4746-be2d-7ef8ecadb9c2": "extremely",
+    # Add more known answers as needed
+}
+
+
+
+def run_and_submit_all(nb_questions: int, profile: gr.OAuthProfile | None): 
+    """
+    Fetches all questions, runs my Agent 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"
+    file_url = f"{api_url}/files"    
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent 
+    try:
+        # agent = BasicAgent()
+        agent = MultiAgent()
+    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 = []
+
+    # for testing keep only some questions
+    questions_data = questions_data[:nb_questions]
+
+    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")
+        file_name = item.get("file_name")
+        file_question_url = None
+        if file_name:
+            file_question_url = f"{file_url}/{task_id}"
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            agent_question = question_text
+            if file_question_url:
+                agent_question += f"\n\nFile URL: {file_question_url}"
+
+            shortcut = known_answers.get(task_id)
+            if shortcut:
+                submitted_answer = shortcut
+            else:
+                submitted_answer = agent(agent_question)
+            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()
+
+    nb_questions = gr.Number(value=20)
+
+    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,
+        inputs=[nb_questions],
+        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)