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app.py

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+# app.py
+
+import gradio as gr
+from postpartum_agent import PostpartumResearchAgent
+import requests
+
+# Instantiate your agent
+agent = PostpartumResearchAgent()
+
+# GAIA API base URL
+GAIA_API_URL = "https://huggingface.co/api/gaia"
+
+def answer_question(question):
+    return agent.run(question)
+
+def submit_to_gaia(username, space_link):
+    # Get questions
+    response = requests.get(f"{GAIA_API_URL}/questions")
+    questions = response.json()
+
+    answers = []
+    for q in questions:
+        task_id = q['task_id']
+        question_text = q['question']
+        agent_answer = agent.run(question_text)
+        answers.append({
+            "task_id": task_id,
+            "submitted_answer": agent_answer
+        })
+
+    payload = {
+        "username": username,
+        "agent_code": space_link,
+        "answers": answers
+    }
+
+    submit_response = requests.post(f"{GAIA_API_URL}/submit", json=payload)
+    result = submit_response.json()
+
+    return f"Submitted! Result: {result}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤱 Postpartum Research Agent")
+    gr.Markdown("Ask any postpartum question and see the AI agent help you!")
+
+    with gr.Row():
+        with gr.Column():
+            question = gr.Textbox(label="Your Question", placeholder="How do I handle postpartum fatigue?")
+            output = gr.Textbox(label="Answer")
+
+        with gr.Column():
+            username = gr.Textbox(label="Your HF Username", placeholder="Your Hugging Face username")
+            space_link = gr.Textbox(label="Your Space Link", placeholder="https://huggingface.co/spaces/YOU/YOUR_SPACE/tree/main")
+            submit_btn = gr.Button("Submit to GAIA")
+            submit_output = gr.Textbox(label="Submission Result")
+
+    ask_btn = gr.Button("Get Answer")
+
+    ask_btn.click(fn=answer_question, inputs=question, outputs=output)
+    submit_btn.click(fn=submit_to_gaia, inputs=[username, space_link], outputs=submit_output)
+
+demo.launch()

postpartum_agent.py

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+# postpartum_agent.py
+
+from transformers import pipeline
+from datasets import Dataset
+from sentence_transformers import SentenceTransformer, util
+from smolagents.agent import Agent
+
+import torch
+
+# Prepare knowledge base
+kb_data = [
+    {
+        "title": "Postpartum Fatigue",
+        "content": "It is normal to feel tired after childbirth. Sleep when your baby sleeps, accept help, and eat balanced meals to maintain your energy."
+    },
+    {
+        "title": "Breastfeeding",
+        "content": "Breastfeed on demand, check for a good latch, drink water, and see a lactation consultant if needed."
+    },
+    {
+        "title": "Postpartum Depression",
+        "content": "If sadness continues for more than two weeks, talk to your doctor. Support groups and therapy can help."
+    },
+    {
+        "title": "Self Care",
+        "content": "Take breaks, talk to loved ones, and ask for help. Caring for yourself helps you care for your baby."
+    },
+    {
+        "title": "Healing",
+        "content": "Rest, hydrate, and attend check-ups to heal well after childbirth."
+    }
+]
+
+kb = Dataset.from_list(kb_data)
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+kb_contents = [d['content'] for d in kb_data]
+kb_embeddings = embedder.encode(kb_contents, convert_to_tensor=True)
+
+qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
+
+class PostpartumResearchAgent(Agent):
+    def __init__(self, name="PostpartumAgent"):
+        super().__init__(name=name)
+
+    def run(self, question: str) -> str:
+        query_embedding = embedder.encode(question, convert_to_tensor=True)
+        cos_scores = util.pytorch_cos_sim(query_embedding, kb_embeddings)[0]
+        top_idx = torch.argmax(cos_scores).item()
+        context = kb_data[top_idx]['content']
+
+        prompt = f"""
+        You are a kind postpartum research assistant.
+        Use the information below to answer the question clearly and kindly.
+
+        Information: {context}
+
+        Question: {question}
+
+        Answer (one short sentence):
+        """
+
+        response = qa_pipeline(prompt, max_length=50, do_sample=False)
+        answer = response[0]['generated_text'].strip()
+        return answer

requirement.txt

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+gradio
+transformers
+datasets
+sentence-transformers
+smolagents
+requests
+torch

🤗 Postpartum Research Agent.md

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+# 🤗 Postpartum Research Agent
+
+This is my final project for the Hugging Face AI Agents Course.
+
+- Built with `smolagents`, `transformers`, and `Gradio`
+- Uses a simple postpartum knowledge base
+- Answers GAIA benchmark questions for the final submission
+- Fully reproducible in this public Space
+
+## 🚀 How to Use
+
+1️⃣ Ask any postpartum question in the left box.  
+2️⃣ See the answer from the agent.  
+3️⃣ Enter your username & Space link, click **Submit to GAIA**, and get your benchmark score!
+
+## 📊 My Goal
+
+Achieve at least **30%** on GAIA benchmark to earn the Certificate!
+
+---
+
+Made with 💙 for new moms.