Delete app.py

app.py

@@ -1,48 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from peft import PeftModel, PeftConfig
-import gradio as gr
-
-PEFT_MODEL_ID = "rishu834763/java-explainer-lora"
-
-config = PeftConfig.from_pretrained(PEFT_MODEL_ID)
-base_model_name = config.base_model_name_or_path
-print(f"Loading base model: {base_model_name}")
-
-model = AutoModelForCausalLM.from_pretrained(
-    base_model_name,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    load_in_4bit=True,  # removes this line only if you upgrade to Pro
-)
-
-model = PeftModel.from_pretrained(model, PEFT_MODEL_ID)
-model = model.merge_and_unload()
-
-tokenizer = AutoTokenizer.from_pretrained(base_model_name)
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-def respond(message, history):
-    messages = []
-    for user, assistant in history:
-        messages.append({"role": "user", "content": user})
-        if assistant:
-            messages.append({"role": "assistant", "content": assistant})
-    messages.append({"role": "user", "content": message})
-
-    output = pipe(messages, max_new_tokens=1024, temperature=0.6, do_sample=True)
-    return output[0]["generated_text"][-1]["content"]
-
-gr.ChatInterface(
-    respond,
-    title="Java Explainer – Your Own Fine-Tuned Model",
-    description="This is 100% your LoRA model, not ChatGPT, not Mistral, not anything else.",
-    examples=[
-        "Explain this Java code in simple terms: public class Hello { public static void main(String[] args) { System.out.println(\"Hello World\"); }}",
-        "What is the difference between == and .equals() in Java?",
-        "Why do we mark methods as static in main?"
-    ]
-).queue().launch()