Delete app.py

app.py

@@ -1,47 +0,0 @@
-import streamlit as st
-
-def main():
-    st.title("Classification de séquence")
-
-    title = st.text_input("Titre")
-    post = st.text_area("Post")
-    comment = st.text_area("Commentaire")
-
-    # Bouton Tester
-    if st.button("Tester"):
-        result = classify(title, post, comment)
-        st.success(result)
-
-def classify(title,post,comment):
-
-    from transformers import DistilBertTokenizer,AutoModelForSequenceClassification
-    import torch
-    state_dict=torch.load(f"./ipcbert")
-    tokenizer=RobertaBertTokenizer.from_pretrained("distil-base-uncased")
-    model =  AutoModelForSequenceClassification.from_pretrained('distil-bert-uncased',
-                                                      problem_type="multi_label_classification", 
-                                                      num_labels=3               
-                                                     )
-    model.load_state_dict(state_dict)
-    device = torch.device("cpu")
-    model.to(device)
-    
-    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()
-