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README.md

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----
-title: Course Creator
-emoji: 📈
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.42.0
-app_file: app.py
-pinned: false
-short_description: Interactive course creator that plans and generates full cou
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Course Creator Space
+
+This private space allows you to plan and generate a full course package from a chat conversation with integrated web research.
+
+## Features
+
+- Chat interface to collect the user's course idea
+- Web research using Tavily API (or SerpAPI if configured)
+- Course planning using the OpenAI Chat API
+- Course package generation with a plan and source citations compressed into a zip file
+
+## Setup
+
+1. Add your API keys in the Space repository secrets:
+   - `OPENAI_API_KEY`
+   - `TAVILY_API_KEY`
+   - optionally set `OPENAI_MODEL`, `TEMPERATURE`, `MAX_OUTPUT_TOKENS`
+2. Install dependencies with `pip install -r requirements.txt` (done automatically on Spaces).
+3. Run the Gradio app with `python app.py`.
+

__init__.py

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+# course_agent package

app.py

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+import os
+import gradio as gr
+from course_agent.planner import plan_course
+from course_agent.generators import generate_course_zip
+from course_agent.searcher import run_web_search
+
+# store chat messages and sources globally
+chat_history = []
+sources = []
+
+def handle_user_message(message):
+    global chat_history
+    chat_history.append({"role": "user", "content": message})
+    # simple display of conversation
+    return "\n\n".join([m["role"] + ': ' + m["content"] for m in chat_history])
+
+def handle_search(query, num_results=5, domain_filter=""):
+    global sources
+    results = run_web_search(query, num_results=num_results, domain_filter=domain_filter)
+    sources.extend(results)
+    # return string summarizing sources
+    return "\n".join([f"{r.get('title','')} - {r.get('url','')}" for r in results])
+
+def handle_generate():
+    plan = plan_course(chat_history, sources)
+    zip_path = generate_course_zip(plan, sources)
+    return zip_path
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Course Agent\nChat about your course idea, search for sources, and generate a course package.")
+    message = gr.Textbox(label="Your message")
+    chat_display = gr.Textbox(label="Conversation", interactive=False)
+    search_query = gr.Textbox(label="Search query")
+    domain_filter = gr.Textbox(label="Domain filter (comma-separated)", placeholder=".edu,.gov,acm.org")
+    search_results = gr.Textbox(label="Search results", interactive=False)
+    generate_btn = gr.Button("Generate Course Package")
+    file_output = gr.File(label="course.zip")
+
+    message.submit(handle_user_message, inputs=message, outputs=chat_display)
+    search_query.submit(handle_search, inputs=search_query, outputs=search_results)
+    generate_btn.click(handle_generate, outputs=file_output)
+
+demo.launch()

generators.py

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+import os
+import json
+import zipfile
+
+
+def generate_course_zip(plan, sources, out_dir="/tmp/course_agent"):
+    """Generate a zip package containing the course plan and sources."""
+    os.makedirs(out_dir, exist_ok=True)
+    plan_path = os.path.join(out_dir, "plan.txt")
+    with open(plan_path, "w") as f:
+        f.write(plan)
+    sources_path = os.path.join(out_dir, "sources.json")
+    with open(sources_path, "w") as f:
+        json.dump(sources, f)
+    zip_path = os.path.join(out_dir, "course.zip")
+    with zipfile.ZipFile(zip_path, "w") as zf:
+        zf.write(plan_path, arcname="plan.txt")
+        zf.write(sources_path, arcname="sources.json")
+    return zip_path

planner.py

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+import os
+import openai
+
+
+def plan_course(messages, sources):
+    """Use OpenAI to plan a course based on messages and sources."""
+    openai.api_key = os.getenv("OPENAI_API_KEY")
+    system_prompt = "You are an expert course planner. Use the conversation and sources to propose a structured plan."
+    formatted_messages = [{"role": "system", "content": system_prompt}]
+    # append conversation messages
+    for msg in messages:
+        formatted_messages.append(msg)
+    # simple call to OpenAI chat completion
+    response = openai.ChatCompletion.create(
+        model=os.getenv("OPENAI_MODEL", "gpt-5-mini"),
+        messages=formatted_messages,
+        temperature=float(os.getenv("TEMPERATURE", "0.7")),
+        max_tokens=int(os.getenv("MAX_OUTPUT_TOKENS", "2048")),
+    )
+    plan_text = response["choices"][0]["message"]["content"]
+    return plan_text

requirements.txt

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+gradio>=3.0.0
+openai>=1.0.0
+tavily-python>=0.3.0
+pydantic>=1.10.0
+python-dotenv>=1.0.0

searcher.py

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+import os
+
+
+def run_web_search(query, num_results=5, domain_filter=""):
+    """Run a web search using Tavily API."""
+    try:
+        from tavily import TavilyClient
+    except ImportError:
+        raise ImportError("Please install tavily-python")
+    api_key = os.getenv("TAVILY_API_KEY")
+    if not api_key:
+        raise ValueError("TAVILY_API_KEY environment variable is required")
+    client = TavilyClient(api_key=api_key)
+    params = {"num": num_results}
+    if domain_filter:
+        # Tavily does not support domain filter directly; pass as search_kwargs if needed
+        params["search_kwargs"] = {"site": domain_filter}
+    results = client.search(query, **params)
+    return results