Update app.py

app.py

@@ -1,5 +1,41 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Base model (LLaMA 3.1 8B) from Meta
+base_model_name = "meta-llama/Llama-3-8B"
+
+# Your fine-tuned LoRA adapter (uploaded to Hugging Face)
+lora_model_name = "starnernj/Early-Christian-Church-Fathers-LLaMA-3.1-Fine-Tuned"
+
+# Load base model
+model = AutoModelForCausalLM.from_pretrained(base_model_name)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(model, lora_model_name)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# Function to generate responses
+def chatbot_response(user_input):
+    inputs = tokenizer(user_input, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=400)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Launch the Gradio chatbot
+interface = gr.Interface(
+    fn=chatbot_response,
+    inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."),
+    outputs="text",
+    title="Early Christian Church Fathers Fine-Tuned LLaMA 3.1 8B with LoRA",
+    description="A chatbot using my fine-tuned LoRA adapter on LLaMA 3.1 8B, tuned on thousands of writings of the early Christian Church Fathers.",
+)
+
+interface.launch()
+
+"""
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
@@ -62,3 +98,4 @@ demo = gr.ChatInterface(
 
 if __name__ == "__main__":
     demo.launch()
+"""