Delete app.py

app.py

@@ -1,52 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import gradio as gr
-import random
-
-# Load model from your uploaded Hugging Face repo
-model_name = "pranav2711/SlangBot"
-
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-# Slang word list to inject flavor
-slang_words = [
-    "fr", "no cap", "sus", "bruh", "lit", "rizz", "cheugy", "mid", "deadass", "lowkey",
-    "highkey", "flex", "based", "pog", "vibe", "yeet", "ghosted", "simp", "drip", "goat",
-    "skrrt", "on god", "bussin", "sheesh", "bet", "gyatt", "slay", "fire", "nah fam"
-]
-
-# Adds slang to base text
-def slangify_response(text):
-    tokens = text.split()
-    return " ".join(
-        [random.choice(slang_words) if random.random() < 0.2 else word for word in tokens]
-    )
-
-# Gradio chat function
-def chat(user_input, history):
-    prompt = f"User: {user_input}\nAI (reply informally with internet slang):"
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-    outputs = model.generate(**inputs, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
-    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    reply = decoded.split("AI (reply informally with internet slang):")[-1].strip()
-    return slangify_response(reply)
-
-# Create the app object
-app = gr.ChatInterface(
-    fn=chat,
-    title="🔥 SlangBot (Gemma-2B)",
-    description="SlangBot is powered by Google's Gemma-2B model and fine-tuned to respond with Gen-Z internet slang. Type anything and get vibey slang responses.",
-    examples=["yo", "what’s your vibe", "who are you", "explain rizz"],
-    theme="soft"
-)
-
-# Required for HF Spaces
-if __name__ == "__main__":
-    app.launch()