Upload app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from peft import PeftModel, PeftConfig
+import gradio as gr
+
+# Define the repository ID
+repo_id = "Miguelpef/bart-base-lora-3DPrompt"  # Replace with your repository name
+
+# Load the PEFT configuration from the Hub
+peft_config = PeftConfig.from_pretrained(repo_id)
+
+# Load the base model from the Hub
+model = AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path)
+
+# Load the tokenizer from the Hub
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+
+# Wrap the base model with PEFT
+model = PeftModel.from_pretrained(model, repo_id)
+
+# Now you can use the model for inference as before
+def generar_prompt_desde_objeto(objeto):
+    prompt = objeto
+    inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
+    outputs = model.generate(**inputs, max_length=100)
+    prompt_generado = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return prompt_generado
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generar_prompt_desde_objeto, 
+    inputs=gr.Textbox(lines=2, placeholder="Enter object description here..."), 
+    outputs="text",
+    title="3D Prompt Generator",
+    description="Generates 3D prompts from object descriptions using a fine-tuned BART model.",
+)
+
+# Launch the interface
+iface.launch()