feat: adding the gradio app.

app.py

@@ -0,0 +1,77 @@
+import asyncio
+
+import gradio as gr
+import nest_asyncio
+from huggingface_hub import login
+
+from src.agent_hackathon.consts import PROJECT_ROOT_DIR
+from src.agent_hackathon.logger import get_logger
+from src.agent_hackathon.multiagent import MultiAgentWorkflow
+
+nest_asyncio.apply()
+
+logger = get_logger(log_name="multiagent", log_dir=PROJECT_ROOT_DIR / "logs")
+
+PRIMARY_HEADING = """# ML Topics Deep Research"""
+SECONDARY_HEADING = """### This multi agent framework searches the web for relevant events, queries a DB containing arxiv ML research papers from Jan 2020 - Jun 6th 2025 for select categories, finds relevant content across different websites to answer the users query.
+
+For more details on the filtered arxiv ds refer [here](https://huggingface.co/datasets/Shamik/arxiv_cs_2020_07_2025)
+"""
+workflow = MultiAgentWorkflow()
+
+_login_done = False
+
+
+def run(
+    query: str, api_key: str, chat_history: list[dict[str, str | None]]
+) -> tuple[str,list[dict[str, str | None]]] | None:
+    global _login_done
+    if not api_key or not api_key.startswith("hf"):
+        raise ValueError("Incorrect HuggingFace Inference API Key")
+    if not _login_done:
+        login(token=api_key)
+        _login_done = True
+    try:
+        result = asyncio.run(workflow.run(user_query=query))
+        chat_history.append({"role": "user", "content": query})
+        chat_history.append({"role": "assistant", "content": result})
+        return "", chat_history
+    except Exception as err:
+        logger.error(f"Error during workflow execution: {err}")
+        return None
+
+
+with gr.Blocks(fill_height=True) as demo:
+    gr.Markdown(value=PRIMARY_HEADING)
+    gr.Markdown(value=SECONDARY_HEADING)
+    gr.Markdown(
+        value="""<span style="color:red"> Please use a 🤗 Inference API Key </span>"""
+    )
+    api_key = gr.Textbox(
+        placeholder="Enter your HuggingFace Inference API KEY HERE",
+        label="🤗 Inference API Key",
+        show_label=True,
+        type="password",
+    )
+    chatbot = gr.Chatbot(
+        type="messages", label="DeepResearch", show_label=True, height=500,
+        show_copy_all_button=True, show_copy_button=True
+    )
+    msg = gr.Textbox(
+        placeholder="Type your message here and press enter...",
+        show_label=True,
+        label="Input",
+        submit_btn=True,
+        stop_btn=True,
+    )
+    clear = gr.ClearButton(components=[msg, chatbot])
+    msg.submit(fn=run, inputs=[msg, api_key, chatbot], outputs=[msg, chatbot])
+
+
+if __name__ == "__main__":
+    demo.queue(max_size=1).launch(share=False)
+
+
+# example queries
+# tell me about reinforcement learning in robotics
+# give me event details on reinforcement learning & robotics