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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from dotenv import load_dotenv
import os
# Load environment variables
load_dotenv()
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
# Function to generate blog content
def generate_blog(topic, keywords):
prompt_template = f"""
You are a technical content writer. Write a detailed and informative blog on the following topic.
Topic: {topic}
Keywords: {keywords}
Make sure the blog covers the following sections:
1. Introduction
2. Detailed Explanation
3. Examples
4. Conclusion
Blog:
"""
input_ids = tokenizer(prompt_template, return_tensors="pt")
outputs = model.generate(**input_ids)
blog_content = tokenizer.decode(outputs[0])
return blog_content
# Gradio interface
iface = gr.Interface(
fn=generate_blog,
inputs=[
gr.Textbox(lines=2, placeholder="Enter the blog topic", label="Blog Topic"),
gr.Textbox(lines=2, placeholder="Enter keywords (comma-separated)", label="Keywords")
],
outputs=gr.Textbox(label="Generated Blog Content"),
title="Technical Blog Generator",
description="Generate a detailed technical blog by providing a topic and relevant keywords."
)
if __name__ == "__main__":
iface.launch(share=True) # Set share=True to generate a public link
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