Add code and requirements

app.py

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+import gradio as gr
+from unsloth import FastLanguageModel
+import torch
+
+# Configuration
+max_seq_length = 2048
+dtype = None  # Auto detection of dtype
+load_in_4bit = True  # Use 4-bit quantization to reduce memory usage
+
+peft_model_name = "limitedonly41/website_mistral7b_v02_1200_finetuned_5_big"
+
+# Load the model and tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=peft_model_name,  # YOUR MODEL YOU USED FOR TRAINING
+    max_seq_length=max_seq_length,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit,
+)
+FastLanguageModel.for_inference(model)  # Enable native 2x faster inference
+
+def return_prediction(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)
+    ans = tokenizer.batch_decode(outputs)[0]
+    ans_pred = ans.split('### Response:')[1].split('<')[0]
+    return ans_pred
+
+def classify_website(site_text):
+    prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+Categorize the website into one of the 3 categories:
+
+1) OTHER
+2) NEWS/BLOG
+3) E-commerce
+
+### Input:
+{site_text}
+
+### Response:"""
+    return return_prediction(prompt)
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_website,
+    inputs="text",
+    outputs="text",
+    title="Website Categorization",
+    description="Categorize a website into one of the 3 categories: OTHER, NEWS/BLOG, or E-commerce."
+)
+
+# Launch the interface
+iface.launch()

requirements.txt

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+gradio
+torch
+unsloth
+transformers
+tqdm