ottimizzazione requirements e versioni per alleggerire

.dockerignore

@@ -6,4 +6,5 @@ env
 .ipynb_checkpoints
 data/
 *.bin
-*.pt
+*.pt
+.pytest_cache

Dockerfile

@@ -1,7 +1,7 @@
 #dockerfile
 
 # Versione di Python
-FROM python:3.12.1
+FROM python:3.12-slim
 
 # Set della working directory  
 WORKDIR /app

app.py

@@ -9,7 +9,7 @@ dataset = LoadDataset()
 
 X = dataset.X
 y = dataset.y
-y_pred = []
+y_pred = model.predict(X)
 
 valori = ['negative', 'neutral', 'positive']
 
@@ -29,5 +29,4 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    # y_pred = model.predict(X)
     demo.launch()

requirements.txt

@@ -1,5 +1,7 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
 transformers
-torch
+torch==2.2.1+cpu
+torchvision==0.17.1+cpu
 pandas
 scikit-learn
 datasets

src/modello.py

@@ -2,8 +2,9 @@ from transformers import pipeline
 
 class Modello :
 
-    # Import del modello da Hugging Face
-    sentiment_task = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
+    def __init__(self) :
+        # Import del modello da Hugging Face
+        self.sentiment_task = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
 
     def predict(self,tweets) :
         # Metodo per le predizioni, prende in input una o più stringhe