Update app.py

app.py

@@ -1,209 +1,122 @@
-""" Basic Agent Evaluation Runner"""
-import os
-import inspect
 import gradio as gr
-import requests
-import pandas as pd
-from langchain_core.messages import HumanMessage
-from agent import build_graph
-
-
-
-# (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:
-    """A langgraph agent."""
-    def __init__(self):
-        print("BasicAgent initialized.")
-        self.graph = build_graph()
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # Wrap the question in a HumanMessage from langchain_core
-        messages = [HumanMessage(content=question)]
-        messages = self.graph.invoke({"messages": messages})
-        answer = messages['messages'][-1].content
-        return answer[14:]
-
-
-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.
-        """
+from datasets import load_dataset, Dataset
+from datetime import datetime
+from datetime import date
+import io
+import os
+from PIL import Image, ImageDraw, ImageFont
+from huggingface_hub import login
+
+login(token=os.environ["HUGGINGFACE_TOKEN"])
+
+# Constants
+SCORES_DATASET = "agents-course/unit4-students-scores"
+CERTIFICATES_DATASET = "agents-course/course-certificates-of-excellence"
+THRESHOLD_SCORE = 30
+
+# Function to check user score
+def check_user_score(username):
+    score_data = load_dataset(SCORES_DATASET, split="train", download_mode="force_redownload")
+    matches = [row for row in score_data if row["username"] == username]
+    return matches[0] if matches else None
+
+# Function to check if certificate entry exists
+def has_certificate_entry(username):
+    cert_data = load_dataset(CERTIFICATES_DATASET, split="train", download_mode="force_redownload")
+    print(username)
+    return any(row["username"] == username for row in cert_data)
+
+# Function to add certificate entry
+def add_certificate_entry(username, name, score):
+    # Load current dataset
+    ds = load_dataset(CERTIFICATES_DATASET, split="train", download_mode="force_redownload")
+    
+    # Remove any existing entry with the same username
+    filtered_rows = [row for row in ds if row["username"] != username]
+
+    # Append the updated/new entry
+    new_entry = {
+        "username": username,
+        "score": score,
+        "timestamp": datetime.now().isoformat()
+    }
+    filtered_rows.append(new_entry)
+
+    # Rebuild dataset and push
+    updated_ds = Dataset.from_list(filtered_rows)
+    updated_ds.push_to_hub(CERTIFICATES_DATASET)
+
+# Function to generate certificate PDF
+def generate_certificate(name, score):
+    """Generate certificate image and PDF."""
+    certificate_path = os.path.join(
+        os.path.dirname(__file__), "templates", "certificate.png"
     )
+    im = Image.open(certificate_path)
+    d = ImageDraw.Draw(im)
 
-    gr.LoginButton()
+    name_font = ImageFont.truetype("Quattrocento-Regular.ttf", 100)
+    date_font = ImageFont.truetype("Quattrocento-Regular.ttf", 48)
+
+    name = name.title()
+    d.text((1000, 740), name, fill="black", anchor="mm", font=name_font)
+
+    d.text((1480, 1170), str(date.today()), fill="black", anchor="mm", font=date_font)
+
+    pdf = im.convert("RGB")
+    pdf.save("certificate.pdf")
+
+    return im, "certificate.pdf"
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+# Main function to handle certificate generation
+def handle_certificate(name, profile: gr.OAuthProfile):
+    if profile is None:
+        return "You must be logged in with your Hugging Face account.", None
 
-    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)
+    username = profile.username
+    user_score = check_user_score(username)
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+    if not user_score:
+        return "You need to complete Unit 4 first.", None, None
+
+    score = user_score["score"]
+
+    if score < THRESHOLD_SCORE:
+        return f"Your score is {score}. You need at least {THRESHOLD_SCORE} to pass.", None, None
+
+    certificate_image, certificate_pdf = generate_certificate(name, score)
+    add_certificate_entry(username, name, score)
+    return "Congratulations! Here's your certificate:", certificate_image, certificate_pdf
+
+# Gradio interface    
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎓 Agents Course - Get Your Final Certificate")
+    gr.Markdown("Welcome! Follow the steps below to receive your official certificate:")
+    gr.Markdown("⚠️ **Note**: Due to high demand, you might experience occasional bugs. If something doesn't work, please try again after a moment!")
+    
+    with gr.Group():
+        gr.Markdown("## ✅ How it works")
+        gr.Markdown("""
+        1. **Sign in** with your Hugging Face account using the button below.  
+        2. **Enter your full name** (this will appear on the certificate).  
+        3. Click **'Get My Certificate'** to check your score and download your certificate.
+        """)
+    gr.Markdown("---")
+    gr.Markdown("📝 **Note**: You must have completed [Unit 4](https://huggingface.co/learn/agents-course/unit4/introduction) and your Agent must have scored **above 30** to get your certificate.")
+
+    gr.LoginButton()
+    with gr.Row():
+        name_input = gr.Text(label="Enter your name (this will appear on the certificate)")
+    generate_btn = gr.Button("Get my certificate")
+    output_text = gr.Textbox(label="Result")
+    cert_image = gr.Image(label="Your Certificate")
+    cert_file = gr.File(label="Download Certificate (PDF)", file_types=[".pdf"])
+
+    generate_btn.click(
+        fn=handle_certificate,
+        inputs=[name_input],
+        outputs=[output_text, cert_image, cert_file]
     )
 
-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)
+demo.launch()
+