Upload space.py

space.py

@@ -3,6 +3,7 @@ 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"
@@ -26,7 +27,16 @@ model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
 model.to(device)
 model.eval()
 
-def sentiment_analysis(review_text):
+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():
@@ -58,7 +68,7 @@ iface = gr.Interface(
         gr.Textbox(label="Reasoning")
     ],
     title="Sentiment Analysis ProfAI",
-    description="Analizza la recensione di un film e restituisce il sentiment (positivo/negativo) e la motivazione."
+    description="Analizza il testo e restituisce il sentiment (positivo/negativo) e la motivazione."
 )
 
 if __name__ == "__main__":