Update app.py

app.py

@@ -0,0 +1,49 @@
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# --- ✅ Load Model & Tokenizer ---
+MODEL_PATH = "rohith-yarramala/asyncapi-assistant-model-merged"
+
+# 🚨 Force CPU mode (NO bitsandbytes, NO quantization)
+device = "cpu"
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.float32,  # ✅ Force CPU-friendly dtype
+    device_map=device,  # ✅ Ensure model is loaded on CPU
+    trust_remote_code=True,  # ✅ Required for custom model code
+    low_cpu_mem_usage=True  # ✅ Reduce CPU memory footprint
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
+model.config.pad_token_id = tokenizer.eos_token_id  # ✅ Avoid generation warnings
+
+print("✅ Model and tokenizer loaded successfully!")
+
+# --- 🚀 Define Chatbot Function ---
+def asyncapi_chatbot(question):
+    inputs = tokenizer(question, return_tensors="pt").to(device)
+    output = model.generate(**inputs, max_length=300, use_cache=False)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# --- 🎨 Gradio UI ---
+css = """
+h1 { text-align: center; font-size: 28px; color: #4CAF50; }
+textarea { font-size: 16px; }
+"""
+
+iface = gr.Interface(
+    fn=asyncapi_chatbot,
+    inputs=gr.Textbox(label="Ask an AsyncAPI Question", placeholder="What is an AsyncAPI schema?"),
+    outputs=gr.Textbox(label="AI Response"),
+    title="AsyncAPI Assistant 🤖",
+    description="Ask any question about AsyncAPI, event-driven architecture, or message brokers.",
+    theme="compact",
+    allow_flagging="never",
+    css=css
+)
+
+# --- 🔥 Launch in Public Mode ---
+iface.launch()