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.gradio/certificate.pem

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Procfile

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+web: python book_generator.py

README.md

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 ---
-title: ILL-Ai-Book Writer
-emoji: 📈
-colorFrom: yellow
-colorTo: blue
+title: iLL-Ai-Book_writer
+app_file: book_generator.py
 sdk: gradio
 sdk_version: 5.9.0
-app_file: app.py
-pinned: false
 ---
+title: iLLAi-book
+app_file: book_generator.py
+sdk: gradio
+sdk_version: 5.9.0
+---
+=======
+---
+title: iLLAi-book
+app_file: book_generator.py
+sdk: gradio
+sdk_version: 5.9.0
+---
+
+# Environment Variables
+
+Before running the application, make sure to set the following environment variables:
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+- `GROQ_API_KEY`: Your Groq API key.
+- `MODEL_NAME`: The name of the model to be used (optional, defaults to "llama-3.1-8b-instant").

book_generator.py

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+import os
+from langchain_groq import ChatGroq
+from langchain.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from typing import Dict
+import gradio as gr  # Import Gradio
+
+# Step 3: Set the environment variable for the Groq API Key
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")  # Use environment variable for API Key
+
+# Step 4: Define helper functions for structured book generation
+def create_book_agent(
+    model_name: str = os.getenv("MODEL_NAME", "llama-3.1-8b-instant"),  # Use environment variable for model name
+    temperature: float = 0.7,
+    max_tokens: int = 16384,  # Increased token limit
+    **kwargs
+) -> ChatGroq:
+    """Create a LangChain agent for book writing."""
+    prompt_template = ChatPromptTemplate.from_messages([
+        ("system", "You are a creative writer. Write high-quality, engaging books for any genre."),
+        ("human", "{input}")
+    ])
+    token = os.getenv("HUGGINGFACE_TOKEN")  # Get Hugging Face token from environment variable
+    llm = ChatGroq(model=model_name, temperature=temperature, max_tokens=max_tokens, token=token, **kwargs)
+    chain = prompt_template | llm | StrOutputParser()
+    return chain
+
+def generate_chapter(title: str, synopsis: str, agent) -> str:
+    """Generate a full chapter given a title and synopsis."""
+    query = f"Write a detailed chapter based on the following synopsis:\n\nTitle: {title}\n\nSynopsis: {synopsis}"
+    try:
+        return agent.invoke({"input": query})
+    except Exception as e:
+        print(f"An error occurred while generating the chapter: {e}")
+        return ""
+
+def write_book(agent, title: str, outline: Dict[str, str]) -> str:
+    """
+    Generate a complete book.
+    
+    Args:
+        agent: The LangChain agent for generating text.
+        title (str): The title of the book.
+        outline (Dict[str, str]): A dictionary with chapter titles as keys and synopses as values.
+
+    Returns:
+        str: The full book as a single string.
+    """
+    book = f"# {title}\n\n"
+    for chapter_title, chapter_synopsis in outline.items():
+        book += f"## {chapter_title}\n\n"
+        chapter_text = generate_chapter(chapter_title, chapter_synopsis, agent)
+        book += chapter_text + "\n\n"
+    return book
+
+# Step 5: Create the agent
+book_agent = create_book_agent()
+
+# Step 6: Gradio interface
+def gradio_interface(api_key: str = ""):
+    """Create a Gradio interface for book generation."""
+    with gr.Blocks() as demo:
+        gr.Markdown("## Book Generator")
+        gr.Markdown("This application was created by iLL-Ai AaronAllton and a team of Groq agents that write books.")  # Updated note
+        book_title = gr.Textbox(label="Book Title")
+        book_outline = gr.Textbox(label="Book Outline (Structured format, e.g., 'Chapter 1: Synopsis 1; Chapter 2: Synopsis 2')")  # Updated prompt
+        generate_button = gr.Button("Generate Book")
+        output = gr.Textbox(label="Generated Book", interactive=False)
+
+        def generate_book_interface(title, outline):
+            try:
+                # Normalize the outline input
+                outline_dict = {}
+                chapters = outline.split(';')  # Split by semicolon for each chapter
+                for chapter in chapters:
+                    if ':' in chapter:
+                        title, synopsis = chapter.split(':', 1)
+                        outline_dict[title.strip()] = synopsis.strip()
+                    else:
+                        # Handle cases where the input might not follow the expected format
+                        outline_dict[chapter.strip()] = "No synopsis provided."
+                
+                print(f"Processed Outline: {outline_dict}")  # Debug statement
+                return write_book(book_agent, title, outline_dict)
+            except Exception as e:
+                return f"An error occurred: {e}"
+
+        generate_button.click(generate_book_interface, inputs=[book_title, book_outline], outputs=output)
+
+    demo.launch(share=True)  # Enable sharing of the Gradio app
+
+if __name__ == "__main__":
+    gradio_interface()

requirements.txt

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+gradio
+langchain
+langchain_groq