Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Initialize the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
+model = AutoModelForCausalLM.from_pretrained('gpt2-large')
+
+def generate_blog(topic, max_length=500, num_return_sequences=1):
+    # Encode the topic as input IDs
+    input_ids = tokenizer.encode(topic, return_tensors='pt')
+    
+    # Generate the blog text
+    outputs = model.generate(
+        input_ids,
+        max_length=max_length,
+        num_return_sequences=num_return_sequences,
+        no_repeat_ngram_size=2,
+        early_stopping=True
+    )
+
+    # Decode the generated IDs to text
+    generated_texts = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
+    return generated_texts
+
+# Example usage
+topic = input(str("Enter the topic:"))
+generated_blogs = generate_blog(topic)
+
+for i, blog in enumerate(generated_blogs):
+    print(f"Blog {i+1}:\n{blog}\n")