New Logic Added

app.py

@@ -1,79 +1,119 @@
 import gradio as gr
-import requests
-import json
+from datasets import load_dataset, Dataset
+from datetime import datetime, date
+import os
+from PIL import Image, ImageDraw, ImageFont
+from huggingface_hub import login
+from transformers import pipeline
+
+# Authenticate with Hugging Face
+login(token=os.environ["HUGGINGFACE_TOKEN"])
 
 # Constants
-QUESTIONS_API = "https://agents-course-unit4-scoring.hf.space/questions"
-SUBMIT_API = "https://agents-course-unit4-scoring.hf.space/submit"
-
-# 1. Fetch evaluation questions from GAIA API
-def fetch_questions():
-    response = requests.get(QUESTIONS_API)
-    if response.status_code == 200:
-        return response.json()
-    else:
-        return []
-
-# 2. Replace this function with your actual agent logic
+SCORES_DATASET = "agents-course/unit4-students-scores"
+CERTIFICATES_DATASET = "agents-course/course-certificates-of-excellence"
+THRESHOLD_SCORE = 30
+
+# Load text-generation pipeline (FLAN-T5)
+qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
+
+# Process question using model
 def process_question(question):
-    # Dummy answer logic — change this with your own model/inference logic
-    return "42" if "life" in question.lower() else "This is a placeholder answer"
+    if not question or not isinstance(question, str):
+        return "I don't understand the question."
+    result = qa_pipeline(question, max_new_tokens=50)
+    return result[0]["generated_text"].strip()
+
+# 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
+
+# Check if user already has certificate
+def has_certificate_entry(username):
+    cert_data = load_dataset(CERTIFICATES_DATASET, split="train", download_mode="force_redownload")
+    return any(row["username"] == username for row in cert_data)
 
-# 3. Submit answers to GAIA API
-def submit_answers(username, agent_code, answers):
-    payload = {
+# Add certificate entry
+def add_certificate_entry(username, name, score):
+    ds = load_dataset(CERTIFICATES_DATASET, split="train", download_mode="force_redownload")
+    filtered_rows = [row for row in ds if row["username"] != username]
+    new_entry = {
         "username": username,
-        "agent_code": agent_code,
-        "answers": answers
+        "score": score,
+        "timestamp": datetime.now().isoformat()
     }
-    headers = {"Content-Type": "application/json"}
-    response = requests.post(SUBMIT_API, data=json.dumps(payload), headers=headers)
-    if response.status_code == 200:
-        return response.json()
-    else:
-        return {"error": f"Submission failed: {response.text}"}
-
-# 4. Main evaluation function
-def run_evaluation(username, agent_code):
-    questions = fetch_questions()
-    if not questions:
-        return "❌ Failed to fetch questions. Please try again."
-
-    answers = []
-    for question in questions:
-        task_id = question.get("task_id")
-        question_text = question.get("question")
-        answer = process_question(question_text)
-        answers.append({
-            "task_id": task_id,
-            "submitted_answer": answer
-        })
-
-    result = submit_answers(username, agent_code, answers)
-
-    if "error" in result:
-        return result["error"]
-
-    score = result.get("score", "N/A")
-    feedback = result.get("feedback", "No feedback provided.")
-    return f"✅ Score: {score}/100\n\n📝 Feedback:\n{feedback}"
-
-# 5. Gradio UI
+    filtered_rows.append(new_entry)
+    updated_ds = Dataset.from_list(filtered_rows)
+    updated_ds.push_to_hub(CERTIFICATES_DATASET)
+
+# Generate certificate
+def generate_certificate(name, score):
+    certificate_path = os.path.join(os.path.dirname(__file__), "templates", "certificate.png")
+    im = Image.open(certificate_path)
+    d = ImageDraw.Draw(im)
+
+    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"
+
+# Handle certificate logic
+def handle_certificate(name, profile: gr.OAuthProfile):
+    if profile is None:
+        return "You must be logged in with your Hugging Face account.", None, None
+
+    username = profile.username
+    user_score = check_user_score(username)
+
+    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 UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 Unit 4 Agent Evaluation")
-    gr.Markdown("Submit your Hugging Face username and agent Space URL below to get your score.")
+    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.Row():
-        username_input = gr.Text(label="Hugging Face Username", placeholder="e.g. your-username")
-        agent_code_input = gr.Text(label="Agent Code URL", placeholder="https://huggingface.co/spaces/your-username/your-agent")
+    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.")
 
-    evaluate_button = gr.Button("🚀 Run Evaluation")
-    output_text = gr.Textbox(label="Result", lines=10)
+    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"])
 
-    evaluate_button.click(
-        fn=run_evaluation,
-        inputs=[username_input, agent_code_input],
-        outputs=output_text
+    generate_btn.click(
+        fn=handle_certificate,
+        inputs=[name_input],
+        outputs=[output_text, cert_image, cert_file]
     )
 
 demo.launch()