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app.py

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+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()

requirements.txt

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+transformers
+torch
+gradio
+accelerate