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app.py

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+import os, sys
+from enum import Enum
+import gradio as gr
+import requests
+import inspect
+import subprocess
+import dateparser
+from bs4 import BeautifulSoup
+import regex
+import pandas as pd
+import torch
+from functools import lru_cache
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from smolagents import CodeAgent, WebSearchTool, WikipediaSearchTool, VisitWebpageTool, PythonInterpreterTool
+import smolagents.tools as _tools
+from smolagents.models import ChatMessage
+# from huggingface_hub import InferenceClient, hf_hub_download
+
+subprocess.run(["playwright", "install"], check=True)
+
+print(dir(_tools))
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# class LocalLLM:
+#     def __init__(self, pipe):
+#         self.pipe = pipe
+
+#     def generate(self, prompt, **kwargs):
+#         unsupported_keys = ["stop_sequences"] # Remove keys not accepted by HF pipelines
+#         cleaned_kwargs = {k: v for k, v in kwargs.items() if k not in unsupported_keys}
+#         # print(f"🧪 kwargs cleaned: {cleaned_kwargs.keys()}")
+#         try:
+#             outputs = self.pipe(prompt, **cleaned_kwargs)
+#             # print(f"🧪 Raw output from pipe: {outputs}")
+#             if isinstance(outputs, list) and isinstance(outputs[0], dict):
+#                 out = outputs[0]["generated_text"]
+#             elif isinstance(outputs, list):
+#                 out = outputs[0]  # fallback if it's just a list of strings
+#             else:
+#                 out = str(outputs)
+#             print("🧪 Final object to return:", type(out), out[:100])
+#             return {'role': 'assistant', 'content': [{'type':'text', 'text': out}]}
+#         except Exception as e:
+#             print(f"❌ Error in LocalLLM.generate(): {e}")
+#             raise
+
+def check_token_access():
+    token = os.environ.get("HF_TOKEN", "")
+    if not token:
+        print("❌ No token found")
+        return
+    headers = {"Authorization": f"Bearer {token}"}
+    url = "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/resolve/main/config.json"
+    try:
+        r = requests.get(url, headers=headers, timeout=10)
+        print(f"🔍 Token test response: {r.status_code}")
+        if r.status_code == 200:
+            print("✅ Token access confirmed for gated model.")
+        elif r.status_code == 403:
+            print("❌ 403 Forbidden: Token does not have access.")
+        else:
+            print("⚠️ Unexpected status:", r.status_code)
+    except Exception as e:
+        print("❌ Token check failed:", e)
+
+class CachedWebSearchTool(WebSearchTool):
+    @lru_cache(maxsize=128)
+    def run(self, query: str):
+        # identical queries return instantly
+        return super().run(query)
+
+class CachedWikiTool(WikipediaSearchTool):
+    @lru_cache(maxsize=128)
+    def run(self, page: str):
+        return super().run(page)
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WHERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self, model_id="meta-llama/Llama-3.1-8B-Instruct", hf_token=""):
+        print("BasicAgent initialized.")
+        print("ENV-HF_TOKEN-LEN", len(hf_token), file=sys.stderr)
+        check_token_access()
+
+        # Local test
+        # client = InferenceClient(
+        # model="meta-llama/Llama-3.1-8B-Instruct",
+        # token=os.environ["HF_TOKEN"]
+        # )
+        # print(client.text_generation("Hello, my name is", max_new_tokens=20))
+        
+        # Initialize the model
+        # model = HfApiModel(model_id="meta-llama/Llama-3.1-8B-Instruct",
+        #                   # format="text-generation",
+        #                   token=os.environ["HF_TOKEN"], 
+        #                   max_tokens=2048,
+        #                   temperature=0.0
+        #                  )
+        
+        # Initialize the tools other than the base tools
+        # See list of base tools in https://github.com/huggingface/smolagents/blob/main/src/smolagents/default_tools.py
+        
+        # Download the model weights and build the pipeline
+        tok = AutoTokenizer.from_pretrained(model_id, token=hf_token)
+        mod = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            torch_dtype=torch.float16,
+            device_map="auto",  # auto-distributes to GPU
+            token=hf_token
+        )
+        self.pipe = pipeline(
+            "text-generation",
+            model=mod,
+            tokenizer=tok,
+            max_new_tokens=512,
+            return_full_text=False,      # <— only get the completion, not the prompt + completion
+            # temperature=1.0,
+        )
+        # Introduce tools
+        wiki_tool = CachedWikiTool()
+        search_tool = CachedWebSearchTool()
+        python_tool = PythonInterpreterTool()
+        html_parse_tool = VisitWebpageTool()
+        # Initialize the agent
+        self.agent = CodeAgent(model=self, 
+                               tools=[wiki_tool, search_tool, python_tool, html_parse_tool], 
+                               add_base_tools=True,
+                               additional_authorized_imports=["dateparser", "bs4", "regex"])
+        
+    def _serialize_messages(self, messages):
+        prompt = []
+        for m in messages:
+            r = m["role"]
+            role = r.value if isinstance(r, Enum) and hasattr(r, "value") else r # "system" / "user" / "assistant"
+            text = "".join([c['text'] for c in m['content']])
+            prompt.append(f"{role}: {text}")
+        return "\n".join(prompt)
+
+    def generate(self, question: str, stop_sequences=None, **kwargs) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # 1. Build the HF kwargs
+        allowed = {"max_new_tokens", "temperature", "top_k", "top_p"}
+        gen_kwargs = {k: v for k, v in kwargs.items() if k in allowed}
+        
+        # 2. Serialize the message and get the response
+        prompt_str = (
+            self._serialize_messages(question)
+            if isinstance(question, list)
+            else question
+        )
+        outputs = self.pipe(prompt_str, **gen_kwargs)
+        response = outputs[0]["generated_text"]
+        # response = self.agent.run(question)
+
+        # 3. Optionally map SmolAgents’ stop_sequences → HF pipeline’s 'stop'
+        if stop_sequences:
+            # find the earliest occurrence of any stop token
+            cuts = [response.find(s) for s in stop_sequences if response.find(s) != -1]
+            if cuts:
+                response = response[: min(cuts)]
+        
+        print(f"Agent returning its generated answer: {response}")
+        
+        # wrap back into a chat message dict
+        return ChatMessage(role="assistant", content=response)
+        # return {
+        #     "role": 'assistant',
+        #     "content": [{"type": "text", "text": response}],
+        # }
+
+    __call__ = generate
+
+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
+    hf_token = os.getenv("HF_TOKEN")
+
+    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(hf_token=hf_token).agent
+    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
+
+    questions_data = questions_data[:5]
+    
+    # 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

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+gradio
+requests
+smolagents[toolkit] @ git+https://github.com/huggingface/smolagents.git@v1.16.1
+torch
+transformers>=4.38.0
+duckduckgo-search
+wikipedia-api
+dateparser
+playwright
+accelerate>=0.24.0
+peft
+bitsandbytes
+beautifulsoup4
+regex