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import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import json
from typing import Tuple
MODEL_ID = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
ADAPTER_REPO = "riccardomusmeci/SentimentProfAI"
SYSTEM_PROMPT = """<|system|>
Analyze the sentiment of the following movie review and label it as positive or negative.
Provide ONLY an output in JSON format with two fields:
- "label": "positive" or "negative"
- "reasoning": a brief explanation of your classification
Do not add any other text after the JSON.</s>"""
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
base_model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16 if device in ["cuda", "mps"] else torch.float32,
)
tokenizer = AutoTokenizer.from_pretrained(ADAPTER_REPO)
model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
model.to(device)
model.eval()
def sentiment_analysis(review_text: str) -> Tuple[str, str]:
"""Analyze the sentiment of a movie review and return the label and reasoning.
Args:
review_text (str): The movie review text to analyze.
Returns:
Tuple[str, str]: A tuple containing the sentiment label ("positive" or "negative") and the reasoning for the classification.
"""
prompt = f"{SYSTEM_PROMPT}<|user|>\n{review_text}</s>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=256,
do_sample=False,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(output[0], skip_special_tokens=True)
# Estrai solo la parte JSON dalla risposta
try:
start = response.index('{')
end = response.rindex('}') + 1
json_str = response[start:end]
sentiment_json = json.loads(json_str)
label = sentiment_json.get("label", "")
reasoning = sentiment_json.get("reasoning", "")
except Exception:
label = "Errore"
reasoning = f"Impossibile estrarre il JSON. Output grezzo: {response}"
return label, reasoning
iface = gr.Interface(
fn=sentiment_analysis,
inputs=gr.Textbox(label="Movie Review"),
outputs=[
gr.Textbox(label="Label"),
gr.Textbox(label="Reasoning")
],
title="Sentiment Analysis ProfAI",
description="Analizza il testo e restituisce il sentiment (positivo/negativo) e la motivazione."
)
if __name__ == "__main__":
iface.launch() |