V6

app.py

@@ -1,45 +1,59 @@
-import os
-import openai
-import gradio as gr
-import transformers
-
-openai.api_key = os.environ["api"]
-
-
-
-generator = transformers.pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
 
-def generateBlogTopics(prompt1):
-    response = generator("Generate blog topics on: {}. \n \n 1.  ".format(prompt1), max_length=100, do_sample=True, temperature=0.7)[0]["generated_text"]
-    return response
 
-def generateBlogSections(prompt1):
-    response = generator("Expand the blog title in to high level blog sections: {} \n\n- Introduction: ".format(prompt1), max_length=100, do_sample=True, temperature=0.6)[0]["generated_text"]
-    return response
-
-def blogSectionExpander(prompt1):
-    response = generator("Expand the blog section in to a detailed professional, witty and clever explanation.\n\n {}".format(prompt1), max_length=400, do_sample=True, temperature=0.7)[0]["generated_text"]
-    return response
-
-input_text = gr.inputs.Textbox(lines=5, label="Enter prompt text here:")
-
-title_section_output = gr.outputs.Textbox(label="Title & Sections")
-section_expander_output = gr.outputs.Textbox(label="Section Expander")
+import gradio as gr
+import openai
+import re
+from transformers import pipeline, set_seed
 
-gr.Interface(
-    gradio.mix.Parallel(generateBlogTopics, generateBlogSections),
-    inputs=input_text,
-    outputs=title_section_output,
-    title="Blog Title & Sections Generator",
-    description="Generate high level sections for your blog topic",
-    live=False
-).launch(share=True)
+# Set up OpenAI API credentials
+openai.api_key = os.environ["api"]
 
-gr.Interface(
-    blogSectionExpander,
-    inputs=input_text,
-    outputs=section_expander_output,
-    title="Blog Section Expander",
-    description="Expand your blog sections with professional and clever explanation",
-    live=False
-).launch(share=True)
+# Set up Hugging Face pipeline for summarization
+summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base")
+
+# Define the function that generates the blog article
+def generate_article(topic):
+    # Use OpenAI's GPT-3 to generate the article
+    prompt = f"Write a blog post about {topic} with 5 different sections."
+    response = openai.Completion.create(
+        engine="davinci",
+        prompt=prompt,
+        max_tokens=2048,
+        n=1,
+        stop=None,
+        temperature=0.5,
+    )
+    article = response.choices[0].text
+
+    # Clean up the article text
+    article = re.sub('\n', ' ', article)
+    article = re.sub('\s+', ' ', article)
+
+    # Split the article into 5 sections
+    section_length = len(article) // 5
+    sections = [article[i:i+section_length] for i in range(0, len(article), section_length)]
+
+    # Set seed for reproducibility in Hugging Face spaces
+    set_seed(42)
+
+    # Summarize each section to generate subheadings
+    subheadings = [summarizer(section, max_length=30, min_length=10, do_sample=False)[0]['summary_text'] for section in sections]
+
+    # Combine the sections and subheadings into a formatted blog post
+    blog_post = f"# {topic}\n\n"
+    for i in range(5):
+        blog_post += f"## {subheadings[i]}\n\n{sections[i]}\n\n"
+    
+    return blog_post
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    generate_article,
+    inputs=gr.inputs.Textbox("Enter a topic for your blog post"),
+    outputs=gr.outputs.HTML(),
+    title="Blog Post Generator",
+    description="Generate a blog post on a given topic with 5 different sections.",
+)
+
+# Launch the interface
+iface.launch(share=True)