Create app.py

app.py

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+
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+import requests
+import torch
+from diffusers import DiffusionPipeline
+
+
+def generate_text(prompt, text_generator):
+    generated_text = text_generator(prompt, max_length=200, num_return_sequences=1, temperature=0.7)[0]['generated_text']
+    return generated_text
+
+def generate_image(prompt, img_gen):
+    generated_image = img_gen(prompt)[0]
+    return generated_image
+
+def generate_blog_post(keywords, text_generator, img_gen):
+    # Text generation
+    generated_text = generate_text(f"Write about {keywords}", text_generator)
+
+    # Image generation
+    generated_image = generate_image(keywords, img_gen)
+
+    return f"# {keywords}\n\n## Introduction\n{generated_text}\n\n## Body\n{generated_text}\n\n## Conclusion\n{generated_text}\n\nGenerated Image: {generated_image}"
+
+def main():
+  
+
+    # Load models
+    text_model_name = "EleutherAI/gpt-neo-1.3B"
+    text_generator = pipeline("text-generation", model=text_model_name, tokenizer=text_model_name)
+
+    img_gen = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
+      # Title of the app
+    st.title("AI Blog Post Generator")
+
+    # User input for keywords
+    user_keywords = st.text_input("Enter keywords for the blog post:")
+
+    # Button to generate blog post
+    if st.button("Generate Blog Post"):
+        # Generate blog post
+        blog_post = generate_blog_post(user_keywords, text_generator, img_gen)
+
+        # Display the generated blog post
+        st.markdown(blog_post, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()