Create app.py

Add Gradio app for intent detection

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from peft import PeftModel
+
+BASE_MODEL = "NousResearch/Llama-2-7b-chat-hf"
+ADAPTER = "Suramya/Llama-2-7b-CloudLex-Intent-Detection"  # your HF model repo (adapters)
+
+# Load base + adapters
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    BASE_MODEL,
+    load_in_4bit=True,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(base_model, ADAPTER)
+
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
+
+clf = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+def predict_intent(message: str):
+    if not message or not message.strip():
+        return {"error": "Please enter a message."}
+    out = clf(message, top_k=6)
+    # return top prediction + scores
+    return {item["label"]: float(item["score"]) for item in out}
+
+demo = gr.Interface(
+    fn=predict_intent,
+    inputs=gr.Textbox(lines=3, placeholder="Type a CloudLex-related message..."),
+    outputs=gr.Label(num_top_classes=6),
+    title="CloudLex Intent Detection (Llama-2 + QLoRA LoRA Adapters)",
+    description="Predicts intent: Buying / Support / Careers / Partnership / Explore / Others"
+)
+
+demo.launch()