Update app.py

app.py

@@ -1,10 +1,34 @@
 import gradio as gr
+import datasets
 
+# Load NVIDIA’s Llama-Nemotron dataset (public)
+dataset = datasets.load_dataset("nvidia/Llama-Nemotron-Post-Training-Dataset-v1", split="train")
+
+# Function to find relevant info from the dataset
+def search_dataset(query):
+    results = []
+    for data in dataset.shuffle(seed=42).select(range(10)):  # Search 10 random samples
+        if query.lower() in data["text"].lower():
+            results.append(data["text"])
+    return "\n\n".join(results) if results else "No relevant data found."
+
+# Function to generate responses
+def chat(user_message):
+    context = search_dataset(user_message)  # Get relevant dataset content
+    system_prompt = "You are Jellyfish AI, an advanced assistant with knowledge from NVIDIA’s dataset."
+    return f"{system_prompt}\nContext: {context}\nUser: {user_message}\nJellyfish AI:"
+
+# Gradio UI
 with gr.Blocks(fill_height=True) as demo:
     with gr.Sidebar():
-        gr.Markdown("# Inference Provider")
-        gr.Markdown("This Space showcases the google/gemma-3-27b-it model, served by the nebius API. Sign in with your Hugging Face account to use this API.")
-        button = gr.LoginButton("Sign in")
-    gr.load("models/google/gemma-3-27b-it", accept_token=button, provider="nebius")
-    
-demo.launch()
+        gr.Markdown("# Jellyfish AI 2025 1.0.0")
+        gr.Markdown("Powered by NVIDIA’s Llama-Nemotron dataset. No external API needed!")
+
+    gr.Markdown("### Chat with Jellyfish AI")
+    user_input = gr.Textbox(label="Your Message")
+    output = gr.Textbox(label="Jellyfish AI's Response", interactive=False)
+
+    chat_button = gr.Button("Send")
+    chat_button.click(chat, inputs=user_input, outputs=output)
+
+demo.launch()