Create app.py

app.py

@@ -0,0 +1,67 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+import pickle
+import streamlit as st
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+
+def init_session_state():
+    if 'history' not in st.session_state:
+        st.session_state.history = ""
+
+# Initialize session state
+init_session_state()
+pipe = pipeline("text2text-generation", model="google/flan-t5-base")
+# model_name = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli"
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
+
+# with open('chapter_titles.pkl', 'rb') as file:
+#     titles_astiko = pickle.load(file)
+# labels1 = ["κληρονομικό", "ακίνητα", "διαζύγιο"]
+# # labels2 = ["αποδοχή κληρονομιάς", "αποποίηση", "διαθήκη"]
+# # labels3 = ["μίσθωση", "κυριότητα", "έξωση", "απλήρωτα νοίκια"]
+
+
+# titles_astiko = ["γάμος", "αλλοδαπός", "φορολογία", "κληρονομικά", "στέγη", "οικογενειακό", "εμπορικό","κλοπή","απάτη"]
+# Load dictionary from the file using pickle
+with open('my_dict.pickle', 'rb') as file:
+    dictionary = pickle.load(file)
+
+def classify(text,labels):
+    output = classifier(text, labels, multi_label=False)
+    
+    return output
+
+
+text = st.text_input('Enter some text:')  # Input field for new text
+
+if text:
+
+    labels = list(dictionary)
+    
+    output = classify(text,labels)
+
+    output = output["labels"][0]
+
+    labels = list(dictionary[output])
+
+    output2 = classify(text,labels)
+
+    output2 = output2["labels"][0]
+
+
+    answer = dictionary[output][output2]
+
+
+    st.session_state.history += "Based on the following information" + answer +"answer this question:" + text  # Add new text to history
+    out = pipe(st.session_state.history)  # Generate output based on history
+    st.text(out[0]['generated_text'])
+    # st.text("History: " + st.session_state.history)
+
+    # st.text(output)
+    # st.text(output2)
+
+    # st.text(answer)