Update app.py

app.py

@@ -10,29 +10,11 @@ MODELS = {
     "GPT2": "datificate/gpt2-small-spanish",
 }
 
-TOKENIZERS = {
-    "T5": None,
-    "LSpanishGPT2": None,
-    "GPT2": None,
-}
-
-# Load Bloom model separately with memory optimizations
-model_bloom = AutoModelForCausalLM.from_pretrained("facebook/xglm-1.7B", low_cpu_mem_usage=True)
-tokenizer_bloom = AutoTokenizer.from_pretrained("facebook/xglm-1.7B")
-
+´
 # Define your function
 def generate_and_analyze(model_name, input_text):
-    # Load the model from the dictionary using the selected model name
-    if model_name == "bloom":
-        #model = model_bloom
-        tokenizer = "" #tokenizer_bloom
-       
-    else:
-        model = MODELS[model_name]
-        tokenizer = TOKENIZERS[model_name]
-        if tokenizer is None:  # Load tokenizer if not already done
-            tokenizer = AutoTokenizer.from_pretrained(model)
-            TOKENIZERS[model_name] = tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model)
+    TOKENIZERS[model_name] = tokenizer
 
     text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0) # Use GPU if available
     result = text_generator(input_text, max_length=250, do_sample=True)[0]