Update app.py

app.py

@@ -1,27 +1,16 @@
-#V02
-
+#V03
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-# Liste des modèles disponibles
-model_list = [
-    "fbaldassarri/tiiuae_Falcon3-1B-Instruct-autogptq-int8-gs128-asym",
-    "MisterAI/jpacifico_Chocolatine-3B-Instruct-DPO-v1.2",
-    # Ajoutez d'autres modèles ici
-]
-
-
-
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
+# Modèle à utiliser
+model_name = "fbaldassarri/tiiuae_Falcon3-1B-Instruct-autogptq-int8-gs128-asym"
 
-def load_model(model_name):
+def load_model():
     """Charge le modèle et le tokenizer"""
-    if model_name is not None and model_name!= "":
-        model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
-        tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-        return model, tokenizer
+    model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    return model, tokenizer
 
 def generate_text(model, tokenizer, input_text, max_length, temperature):
     """Génère du texte en utilisant le modèle"""
@@ -29,25 +18,17 @@ def generate_text(model, tokenizer, input_text, max_length, temperature):
     output = model.generate(**inputs, max_length=max_length, temperature=temperature)
     return tokenizer.decode(output[0], skip_special_tokens=True)
 
-def main(model_name, input_text, max_length, temperature):
+def main(input_text, max_length, temperature):
     """Fonction principale pour générer le texte"""
-    if model_name is not None and model_name!= "":
-        model, tokenizer = load_model(model_name)
-        generated_text = generate_text(model, tokenizer, input_text, max_length, temperature)
-        return generated_text
-    else:
-        return "Veuillez sélectionner un modèle"
+    model, tokenizer = load_model()
+    generated_text = generate_text(model, tokenizer, input_text, max_length, temperature)
+    return generated_text
 
 demo = gr.Blocks()
 
 with demo:
     gr.Markdown("# Modèle de Langage")
     
-    with gr.Row():
-        model_select = gr.Dropdown(model_list, label="Sélectionner un modèle")
-    with gr.Row():
-        load_button = gr.Button("Charger le modèle")
-        
     with gr.Row():
         input_text = gr.Textbox(label="Texte d'entrée")
     with gr.Row():
@@ -57,18 +38,10 @@ with demo:
         submit_button = gr.Button("Soumettre")
         
     output_text = gr.Textbox(label="Texte généré")
-    history = gr.JSON(label="Historique")
-    
-    load_button.click(
-        load_model,
-        inputs=model_name,
-        outputs=None,
-        queue=False
-    )
     
     submit_button.click(
         main,
-        inputs=[model_name, input_text, max_length_slider, temperature_slider],
+        inputs=[input_text, max_length_slider, temperature_slider],
         outputs=output_text,
         queue=False
     )