Update app.py

app.py

@@ -1,18 +1,24 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
 
-# Initialize the text-generation pipeline with a pre-trained model (e.g., GPT-2)
-generator = pipeline("text-generation", model="gpt-2")
+# Load pre-trained model and tokenizer
+model_name = "gpt-2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
 
+# Function to generate a blog post
 def generate_blogpost(topic):
     prompt = f"Write a detailed blog post about {topic}."
-    result = generator(prompt, max_length=300, num_return_sequences=1)
-    return result[0]['generated_text']
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=300, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
 
 # Define the Gradio interface
 iface = gr.Interface(
     fn=generate_blogpost,
-    inputs=gr.Textbox(lines=2, placeholder="Enter blog topic here..."),
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter blog topic here..."),
     outputs="text",
     title="Blog Post Generator",
     description="Generate a detailed blog post for a given topic using GPT-2."