update app.py

app.py

@@ -1,7 +1,8 @@
+
 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
@@ -10,4 +11,35 @@ text = st.text_input('Entrer votre texte') #text is stored in this variable
 out = pipe(text)
 
 st.write("Sentiment du text: ")
-st.write(out)
+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'])
+