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app.py

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+import streamlit as st
+from transformers import RobertaTokenizer,AutoModelForSequenceClassification
+import torch
+
+state_dict=torch.load("models/fine_tuned_roberta.bin",map_location=torch.device("cpu"))
+tokenizer=RobertaTokenizer.from_pretrained("roberta-base")
+model =  AutoModelForSequenceClassification.from_pretrained('roberta-base',
+                                                      problem_type="multi_label_classification", 
+                                                      num_labels=3               
+                                                     )
+model.load_state_dict(state_dict)
+device = torch.device("cpu")
+model.to(device)
+
+
+def main():
+    st.title("Classification de séquence")
+
+    title = st.text_input("Titre")
+    post = st.text_area("Post")
+    comment = st.text_area("Commentaire")
+
+    if st.button("Tester"):
+        result = get_predictions(title, post, comment)
+        st.success(result)
+@st.cache_data
+def get_predictions(title, post, commentaire):
+    model.eval()
+    inputs = tokenizer("title of the post: " + title + "\n" + "post: " + post + "\n" + "comment: " + commentaire, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    input_ids = inputs['input_ids'].to(device)
+    attention_mask = inputs['attention_mask'].to(device)
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_mask)
+        logits = outputs.logits
+        _, preds = torch.max(logits, dim=1)
+    id2label = {
+        0: "neutral",
+        1: "with palestine",
+        2: "with israel"
+    }
+    return id2label[preds.item()]
+if __name__ == "__main__":
+    main()