Create app.py

app.py

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+import gradio as gr
+import transformers as t
+import torch
+
+# Load your fine-tuned model and tokenizer
+model = t.AutoModelForCausalLM.from_pretrained("./weights")
+tokenizer = t.AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
+tokenizer.pad_token_id = 0
+
+# Define a prediction function
+def generate_article(title):
+    prompt = f"Below is a title for an article. Write an article that appropriately suits the title: \n\n### Title:\n{title}\n\n### Article:\n"
+    pipe = t.pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=1000)
+    output = pipe([prompt])
+    generated_article = output[0][0]["generated_text"]
+    return generated_article
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_article,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter Article Title Here"),
+    outputs="text",
+    title="Article Generator",
+    description="Enter a title to generate an article."
+)
+
+# Launch the app
+iface.launch()