app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

print("Loading model...")

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)

base_model_name = "unsloth/Llama-3.2-1B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(base_model, "AA65327/lora_model")

print("Model loaded!")

def classify_emotion(text):
    prompt = f"Classify the emotion in this text: {text}\n\nEmotion:"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.3, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    emotion = response.split("Emotion:")[-1].strip().split()[0]
    return emotion

demo = gr.Interface(
    fn=classify_emotion,
    inputs=gr.Textbox(label="Enter text to classify", placeholder="I am so happy today!"),
    outputs=gr.Textbox(label="Detected Emotion"),
    title="Emotion Classifier",
    description="Classify emotions in text using a fine-tuned Llama model"
)

demo.launch()