Create app.py

app.py

@@ -0,0 +1,35 @@
+import torch
+import gradio as gr
+from peft import PeftModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Base model and your LoRA adapter
+base_model = "mistralai/Mistral-7B-Instruct-v0.1"
+adapter_repo = "gaurav2003/room-service-chatbot"
+
+# Load tokenizer and base model
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load your LoRA adapter
+model = PeftModel.from_pretrained(model, adapter_repo)
+model.eval()
+
+# Chat function
+def generate_response(message, history):
+    input_text = message
+    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=512)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Gradio Interface
+chatbot = gr.ChatInterface(fn=generate_response, title="Room Service Chatbot")
+
+if __name__ == "__main__":
+    chatbot.launch()