Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from peft import PeftModel
+import gradio as gr
+from huggingface_hub import login
+
+# Log in with the secret token (stored in Hugging Face Secrets)
+login(token="${HF_TOKEN}")
+
+# Define model paths
+base_model_name = "meta-llama/Llama-3.2-3B-Instruct"
+lora_adapter_path = "agilan1102/eysflow_adapters"
+
+# Load tokenizer and models
+tokenizer = AutoTokenizer.from_pretrained(base_model_name, use_auth_token=True)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    device_map="auto",
+    use_auth_token=True
+)
+model_with_adapter = PeftModel.from_pretrained(base_model, lora_adapter_path, use_auth_token=True)
+
+def generate_text_adapter(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model_with_adapter.device)
+    outputs = model_with_adapter.generate(**inputs, max_new_tokens=500)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=generate_text_adapter,
+    inputs="text",
+    outputs="text",
+    title="My Finetuned LLM API"
+)
+
+# Launch the interface
+demo.launch()