Update app.py

app.py

@@ -1,287 +1,287 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-from smolagents import DuckDuckGoSearchTool, HfApiModel, PythonInterpreterTool, VisitWebpageTool, CodeAgent
-import hashlib
-import json
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, TransformersEngine
-import wikipedia
-import torch
-from tooling import MathModelQuerer, WikipediaPageFetcher, YoutubeTranscriptFetcher, CodeModelQuerer
-
-# (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 ------
-import os
-
-cache = {}
-
-web_search = DuckDuckGoSearchTool()
-python_interpreter = PythonInterpreterTool()
-visit_webpage_tool = VisitWebpageTool()
-wiki_tool = WikipediaPageFetcher()
-yt_transcript_fetcher = YoutubeTranscriptFetcher()
-math_model_querer = MathModelQuerer()
-code_model_querer = CodeModelQuerer()
-
-
-def load_cached_answer(question_id: str) -> str:
-    if question_id in cache.keys():
-        return cache[question_id]
-    else:
-        return None
-
-
-def cache_answer(question_id: str, answer: str):
-    cache[question_id] = answer
-
-
-# --- Model Setup ---
-MODEL_NAME = 'Qwen/Qwen2.5-3B-Instruct'  # 'meta-llama/Llama-3.2-3B-Instruct'
-
-
-# "Qwen/Qwen2.5-VL-3B-Instruct"#'meta-llama/Llama-2-7b-hf'#'meta-llama/Llama-3.1-8B-Instruct'#'TinyLlama/TinyLlama-1.1B-Chat-v1.0'#'mistralai/Mistral-7B-Instruct-v0.2'#'microsoft/DialoGPT-small'# 'EleutherAI/gpt-neo-2.7B'#'distilbert/distilgpt2'#'deepseek-ai/DeepSeek-R1-Distill-Qwen-7B'#'mistralai/Mistral-7B-Instruct-v0.2'
-
-
-def load_model(model_name):
-    """Download and load the model and tokenizer."""
-    try:
-        print(f"Loading model {MODEL_NAME}...")
-        model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
-        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-
-        if tokenizer.pad_token is None:
-            tokenizer.pad_token = tokenizer.eos_token
-
-        print(f"Model {MODEL_NAME} loaded successfully.")
-
-        transformers_engine = TransformersEngine(pipeline("text-generation", model=model, tokenizer=tokenizer))
-
-        return transformers_engine, model
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        raise
-
-
-# Load the model and tokenizer locally
-# model, tokenizer = load_model()
-model = HfApiModel()  # model_id=MODEL_NAME, max_tokens=512)
-model_id = "reedmayhew/claude-3.7-sonnet-reasoning-gemma3-12B"  # "microsoft/phi-2"# not working out of the box"google/gemma-2-2b-it" #toobig"Qwen/Qwen1.5-7B-Chat"#working but stupid: "meta-llama/Llama-3.2-3B-Instruct"
-model = HfApiModel(model_id)
-#from smolagents import TransformersModel
-# model = TransformersModel(
-#     model_id=model_id,
-#     max_new_tokens=256)
-
-# model = HfApiModel()
-
-
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-        self.agent = CodeAgent(
-            model=model,
-            tools=[web_search, python_interpreter, visit_webpage_tool, wiki_tool, code_model_querer, math_model_querer],
-            max_steps=3,
-            verbosity_level=1,
-            grammar=None,
-            planning_interval=3,
-            add_base_tools=True,
-            additional_authorized_imports=['requests', 'wikipedia', 'pandas']
-
-        )
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        answer = self.agent.run(question)
-        return 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[:1]:
-        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:
-            cached = load_cached_answer(task_id)
-            if cached:
-                submitted_answer = cached
-                print(f"Loaded cached answer for task {task_id}")
-            else:
-                submitted_answer = agent(question_text)
-                cache_answer(task_id, submitted_answer)
-                print(f"Generated and cached answer for task {task_id}")
-
-            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)
+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+from smolagents import DuckDuckGoSearchTool, HfApiModel, PythonInterpreterTool, VisitWebpageTool, CodeAgent
+import hashlib
+import json
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, TransformersEngine
+import wikipedia
+import torch
+from tooling import MathModelQuerer, WikipediaPageFetcher, YoutubeTranscriptFetcher, CodeModelQuerer
+
+# (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 ------
+import os
+
+cache = {}
+
+web_search = DuckDuckGoSearchTool()
+python_interpreter = PythonInterpreterTool()
+visit_webpage_tool = VisitWebpageTool()
+wiki_tool = WikipediaPageFetcher()
+yt_transcript_fetcher = YoutubeTranscriptFetcher()
+math_model_querer = MathModelQuerer()
+code_model_querer = CodeModelQuerer()
+
+
+def load_cached_answer(question_id: str) -> str:
+    if question_id in cache.keys():
+        return cache[question_id]
+    else:
+        return None
+
+
+def cache_answer(question_id: str, answer: str):
+    cache[question_id] = answer
+
+
+# --- Model Setup ---
+MODEL_NAME = 'Qwen/Qwen2.5-3B-Instruct'  # 'meta-llama/Llama-3.2-3B-Instruct'
+
+
+# "Qwen/Qwen2.5-VL-3B-Instruct"#'meta-llama/Llama-2-7b-hf'#'meta-llama/Llama-3.1-8B-Instruct'#'TinyLlama/TinyLlama-1.1B-Chat-v1.0'#'mistralai/Mistral-7B-Instruct-v0.2'#'microsoft/DialoGPT-small'# 'EleutherAI/gpt-neo-2.7B'#'distilbert/distilgpt2'#'deepseek-ai/DeepSeek-R1-Distill-Qwen-7B'#'mistralai/Mistral-7B-Instruct-v0.2'
+
+
+def load_model(model_name):
+    """Download and load the model and tokenizer."""
+    try:
+        print(f"Loading model {MODEL_NAME}...")
+        model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+
+        print(f"Model {MODEL_NAME} loaded successfully.")
+
+        transformers_engine = TransformersEngine(pipeline("text-generation", model=model, tokenizer=tokenizer))
+
+        return transformers_engine, model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        raise
+
+
+# Load the model and tokenizer locally
+# model, tokenizer = load_model()
+model = HfApiModel()  # model_id=MODEL_NAME, max_tokens=512)
+model_id = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"  # "microsoft/phi-2"# not working out of the box"google/gemma-2-2b-it" #toobig"Qwen/Qwen1.5-7B-Chat"#working but stupid: "meta-llama/Llama-3.2-3B-Instruct"
+model = HfApiModel(model_id)
+#from smolagents import TransformersModel
+# model = TransformersModel(
+#     model_id=model_id,
+#     max_new_tokens=256)
+
+# model = HfApiModel()
+
+
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.agent = CodeAgent(
+            model=model,
+            tools=[web_search, python_interpreter, visit_webpage_tool, wiki_tool, code_model_querer, math_model_querer],
+            max_steps=3,
+            verbosity_level=1,
+            grammar=None,
+            planning_interval=3,
+            add_base_tools=True,
+            additional_authorized_imports=['requests', 'wikipedia', 'pandas']
+
+        )
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        answer = self.agent.run(question)
+        return 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[:1]:
+        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:
+            cached = load_cached_answer(task_id)
+            if cached:
+                submitted_answer = cached
+                print(f"Loaded cached answer for task {task_id}")
+            else:
+                submitted_answer = agent(question_text)
+                cache_answer(task_id, submitted_answer)
+                print(f"Generated and cached answer for task {task_id}")
+
+            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)