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# from transformers import pipeline
# generator = pipeline("text-generation", model="gpt2")
# def generate_blog(topic):
# return generator(f"Write a blog on: {topic}", max_length=200)[0]["generated_text"]
# from transformers import pipeline
# import gradio as gr
# # Load the model
# generator = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1")
# # Define your function
# def generate_text(prompt):
# result = generator(prompt, max_length=100, num_return_sequences=1, do_sample=True,
# temperature=0.7,
# top_p=0.9,)
# return result[0]["generated_text"]
# # Create Gradio interface
# iface = gr.Interface(fn=generate_text, inputs="text", outputs="text", title="Text Generator with GPT-2")
# # ✅ This line is required to actually launch the app on Hugging Face Spaces
# iface.launch()
from transformers import pipeline
import gradio as gr
generator = pipeline("text-generation", model="sshleifer/tiny-gpt2")
def clean_topic(topic):
topic = topic.lower()
if "write a blog on" in topic:
topic = topic.replace("write a blog on", "").strip()
elif "write a blog about" in topic:
topic = topic.replace("write a blog about", "").strip()
return topic.capitalize()
# def generate_blog(topic):
# topic = clean_topic(topic)
# if not topic:
# return "Please provide a topic."
# prompt = f"""
# Write a detailed and engaging blog post about "{topic}".
# Include an introduction, 2–3 subheadings with paragraphs, and a conclusion.
# Make it informative and conversational.
# """
# result = generator(prompt, max_length=700, do_sample=True, temperature=0.7, top_p=0.9)
# return result[0]['generated_text']
def generate_blog(topic):
prompt = f"""
Write a detailed and engaging blog post about "{topic}".
Include an introduction, 2–3 subheadings with paragraphs, and a conclusion.
Make it informative and conversational.
"""
result = generator(prompt, max_length=700, do_sample=True, temperature=0.7, top_p=0.9)
return result[0]['generated_text']
gr.Interface(fn=generate_blog, inputs="text", outputs="text", title="AI Blog Writer").launch()
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