Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# ------------------------------
+# Load Model & Tokenizer
+# ------------------------------
+model_name = "kaitchup/Llama-3.2-3B-Instruct-educational-chatbot"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# ------------------------------
+# Define Chat Function
+# ------------------------------
+def chat_with_ai(user_input, history):
+    history = history or []
+    messages = [f"User: {msg[0]}\nAI: {msg[1]}" for msg in history]
+    messages.append(f"User: {user_input}\nAI:")
+    prompt = "\n".join(messages)
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
+    reply = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Extract the latest AI response
+    if "AI:" in reply:
+        reply = reply.split("AI:")[-1].strip()
+
+    history.append((user_input, reply))
+    return reply, history
+
+# ------------------------------
+# Create Gradio Chat Interface
+# ------------------------------
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("<h2 style='text-align:center'>🎓 EduBridge AI Chatbot</h2>")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Ask me anything educational...")
+    clear = gr.Button("Clear Chat")
+
+    msg.submit(chat_with_ai, [msg, chatbot], [chatbot, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+# ------------------------------
+# Launch App
+# ------------------------------
+demo.launch()