Create app.py

app.py

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+import gradio as gr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load pre-trained model and tokenizer
+model_name = "gpt2"  # You can use other models like gpt-2-large or gpt-3 for better performance
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+# Function to generate keywords based on a prompt
+def generate_keywords(prompt):
+    # Encode input prompt
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+
+    # Generate output from model
+    outputs = model.generate(inputs, max_length=50, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95)
+
+    # Decode generated tokens
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extracting keywords by splitting the generated text into words
+    keywords = generated_text.split()
+    
+    return " ".join(keywords)
+
+# Gradio interface
+iface = gr.Interface(fn=generate_keywords, 
+                     inputs=gr.Textbox(label="Enter Ad Prompt", placeholder="E.g., Generate ad keywords for wireless headphones"),
+                     outputs=gr.Textbox(label="Generated Keywords"),
+                     live=True)
+
+# Launch interface
+iface.launch()