Upload app.py

app.py

@@ -1,102 +1,101 @@
-from huggingface_hub import InferenceClient
-import gradio as gr
-
-client = InferenceClient(
-    "mistralai/Mistral-7B-Instruct-v0.1"
-)
-
-
-def format_prompt(message, history):
-  prompt = "<s>"
-  for user_prompt, bot_response in history:
-    prompt += f"[INST] {user_prompt} [/INST]"
-    prompt += f" {bot_response}</s> "
-  prompt += f"[INST] {message} [/INST]"
-  return prompt
-
-def generate(
-    prompt, history, temperature=0.7, max_new_tokens=256, top_p=0.95, repetition_penalty=1.1,
-):
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42,
-    )
-
-    formatted_prompt = format_prompt(prompt, history)
-
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    output = ""
-
-    for response in stream:
-        output += response.token.text
-        yield output
+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")
+
+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()
+
+pipe = pipeline("text2text-generation", model="google/flan-t5-base")
+# pipe = pipeline("text-generation", model="GeneZC/MiniChat-1.5-3B")
+# pipe = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.2")
+# 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
 
 
-additional_inputs=[
-    gr.Slider(
-        label="Temperature",
-        value=0.7,
-        minimum=0.0,
-        maximum=1.0,
-        step=0.05,
-        interactive=True,
-        info="Higher values produce more diverse outputs",
-    ),
-    gr.Slider(
-        label="Max new tokens",
-        value=256,
-        minimum=0,
-        maximum=1024,
-        step=64,
-        interactive=True,
-        info="The maximum numbers of new tokens",
-    ),
-    gr.Slider(
-        label="Top-p (nucleus sampling)",
-        value=0.95,
-        minimum=0.0,
-        maximum=1,
-        step=0.05,
-        interactive=True,
-        info="Higher values sample more low-probability tokens",
-    ),
-    gr.Slider(
-        label="Repetition penalty",
-        value=1.1,
-        minimum=1.0,
-        maximum=2.0,
-        step=0.05,
-        interactive=True,
-        info="Penalize repeated tokens",
-    )
-]
-
-css = """
-  #mkd {
-    height: 500px; 
-    overflow: auto; 
-    border: 1px solid #ccc; 
-  }
-"""
-
-with gr.Blocks(css=css) as demo:
-    gr.HTML("<h1><center>Mistral 7B Instruct<h1><center>")
-    gr.HTML("<h3><center>In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 💬<h3><center>")
-    gr.HTML("<h3><center>Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚<h3><center>")
-    gr.ChatInterface(
-        generate,
-        additional_inputs=additional_inputs,
-        examples=[["What is the secret to life?"], ["Write me a recipe for pancakes."]]
-    )
-
-demo.queue().launch()
+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')
+
+    st.text("H ερώτηση σας σχετίζεται με " + output+ " δίκαιο")
+ 
+
+# Translate the text from Greek to English
+    answer = translator.translate(answer)
+    text = translator.translate(text)
+
+    st.text("Πιο συγκεκριμένα σχετίζεται με " + output2)
+
+
+# text_to_translate2 = text[499:999]
+# translated_text2 = translator.translate(text_to_translate2)
+
+    
+
+    st.session_state.history += "Based on this info only:" + answer +" ,answer this question, by reasoning step by step:" + text  # Add new text to history
+    out = pipe(st.session_state.history, max_new_tokens=256)  # Generate output based on history
+
+
+    # st.text(st.session_state.history)
+    
+    translated_text2 = translator2.translate(out[0]['generated_text'])
+    
+
+    st.text(translated_text2)
+
+    # with st.expander("View Full Output", expanded=False):
+    #     st.write(translated_text2, allow_output_mutation=True)
+
+    # st.text(translated_text2)
+    # st.text("History: " + st.session_state.history)
+
+    # st.text(output)
+    # st.text(output2)
+
+    # st.text(answer)