Update app.py

app.py

@@ -1,45 +1,20 @@
 
 import streamlit as st
 from transformers import pipeline
-from textblob import TextBlob
 """"
 pipe = pipeline('sentiment-analysis')
 st.title("Analyse de sentiment")
 #Textbox for text user is entering
-text = st.text_input('Entrer votre texte') #text is stored in this variable
+
 
 out = pipe(text)
 
 st.write("Sentiment du text: ")
 st.write(out)
 """
-
-import transformers
-import torch
-
-model_name = "OpenLLM-France/Claire-7B-0.1"
-
-tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-model = transformers.AutoModelForCausalLM.from_pretrained(model_name,
-    device_map="auto",
-    torch_dtype=torch.bfloat16,
-    load_in_4bit=True                          # For efficient inference, if supported by the GPU card
-)
-
-pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer)
-generation_kwargs = dict(
-    num_return_sequences=1,                    # Number of variants to generate.
-    return_full_text= False,                   # Do not include the prompt in the generated text.
-    max_new_tokens=200,                        # Maximum length for the output text.
-    do_sample=True, top_k=10, temperature=1.0, # Sampling parameters.
-    pad_token_id=tokenizer.eos_token_id,       # Just to avoid a harmless warning.
-)
-
-prompt = """\
-- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-- Bonjour Camille,\
-"""
-completions = pipeline(prompt, **generation_kwargs)
-for completion in completions:
-    print(prompt + " […]" + completion['generated_text'])
-
+classifier = pipeline("zero-shot-classification",
+                      model="morit/french_xlm_xnli")
+text = st.text_input('Entrer le texte a analyser') 
+andidate_labels = ["politique", "sport"]
+hypothesis_template = "Cet example est {}"
+st.write(classifier(text, candidate_labels, hypothesis_template=hypothesis_template))