app.py

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")

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-rw-1b", trust_remote_code=True)

# 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", max_length=512, truncation=True)
    outputs = model.generate(input_ids["input_ids"], max_length=800, num_return_sequences=1)
    blog_content = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    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