Update app.py

app.py

@@ -1,23 +1,105 @@
-# Install transformers from source - only needed for versions <= v4.34
-# pip install git+https://github.com/huggingface/transformers.git
-# pip install accelerate
-
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import torch
-from transformers import pipeline
+import pickle
+import streamlit as st
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+from translate import Translator
+
+def init_session_state():
+    if 'history' not in st.session_state:
+        st.session_state.history = ""
 
+# Initialize session state
+init_session_state()
+# 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")
 pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
 
-# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
-messages = [
+# 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]
+
+    # Create a translator object with specified source and target languages
+    translator = Translator(from_lang='el', to_lang='en')
+    translator2 = Translator(from_lang='en', to_lang='el')
+
+ 
+
+# Translate the text from Greek to English
+    answer = translator.translate(answer)
+    text = translator.translate(text)
+
+# text_to_translate2 = text[499:999]
+# translated_text2 = translator.translate(text_to_translate2)
+
+
+
+    st.session_state.history += "Based on the following information" + answer +"answer this question:" + text + "by reasoning step by step"  # Add new text to history
+    # out = pipe(st.session_state.history)  # Generate output based on history
+
+    messages = [
     {
         "role": "system",
-        "content": "You are a friendly chatbot",
+        "content": "You are a friendly chatbot who answers question based on the info that I give you:" + answer,
     },
-    {"role": "user", "content": "I'm 29 years old and I live in Greece. Tellm me how old am I"},
-]
-prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
-print(outputs[0]["generated_text"])
+    {"role": "user", "content": text"},
+    ]
+    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+
+
+
+    st.text(st.session_state.history)
+    
+    translated_text2 = translator2.translate(outputs)
+
+    st.text(translated_text2)
+    # st.text("History: " + st.session_state.history)
+
+    # st.text(output)
+    # st.text(output2)
+
+    # st.text(answer)
+
+# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
+
 # <|system|>
 # You are a friendly chatbot who always responds in the style of a pirate.</s>
 # <|user|>