Upload app.py

app.py

@@ -1,22 +1,11 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-import torch
-import pickle
-import streamlit as st
-from translate import Translator
-
 from huggingface_hub import InferenceClient
 import gradio as gr
 
+client = InferenceClient(
+    "mistralai/Mistral-7B-Instruct-v0.1"
+)
 
 
-
-
-
-def classify(text,labels):
-    output = classifier(text, labels, multi_label=False)
-    
-    return output
-
 def format_prompt(message, history):
   prompt = "<s>"
   for user_prompt, bot_response in history:
@@ -53,89 +42,6 @@ def generate(
     return output
 
 
-client = InferenceClient(
-    "mistralai/Mistral-7B-Instruct-v0.1"
-)
-
-classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
-
-with open('my_dict.pickle', 'rb') as file:
-    dictionary = pickle.load(file)
-
-    
-# text = st.text_input('Enter some text:')  # Input field for new 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)
-    
-
-    
-
-#     prompt =  "Based on this info only:" + answer +" ,answer this question, by reasoning step by step:" + text
-#     formatted_prompt = format_prompt(prompt, history)
-    # translated_text2 = translator2.translate(output)
-
-
-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
-    return output
-    
-
-
-
-
-
-
-
 additional_inputs=[
     gr.Slider(
         label="Temperature",