app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch # Import torch for device management
import os # For file operations

# --- Configuration and Model Loading ---
# You can choose a different model here if you have access to more powerful ones.
# For larger models, ensure you have sufficient VRAM (GPU memory).
# For CPU, smaller models might be necessary or use quantization.
MODEL_NAME = "google/flan-t5-large" # Changed to 'large' for slightly better performance than 'base' and still manageable.
# If you have a powerful GPU, consider "google/flan-t5-xl" or even "google/flan-t5-xxl"
# For even larger models, consider using model.to(torch.bfloat16) or bitsandbytes for 4-bit loading if available.

try:
    # Determine the device to use (GPU if available, else CPU)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Loading model on device: {device}")

    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    # Load model with half-precision (float16) to save VRAM if on GPU
    # Or load in 8-bit/4-bit if using libraries like bitsandbytes (requires installation)
    if device == "cuda":
        model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16).to(device)
    else:
        model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME).to(device)
    
    model.eval() # Set model to evaluation mode
    print(f"Model '{MODEL_NAME}' loaded successfully.")

except Exception as e:
    print(f"Error loading model: {e}")
    print("Please check your internet connection, model name, and available resources (RAM/VRAM).")
    # Exit or handle gracefully if model loading fails
    tokenizer, model = None, None

# --- Prompt Engineering Functions (more structured) ---

def create_arabic_prompt(topic, style):
    if style == "Blog Post (Descriptive)":
        return f"اكتب مقالاً احترافياً بأسلوب شخصي عن: {topic}. ركز على التفاصيل، الوصف الجذاب، قدم نصائح عملية. اجعل النص منسقاً بفقرات وعناوين فرعية."
    elif style == "Social Media Post (Short & Catchy)":
        return f"اكتب منشوراً قصيراً وجذاباً ومثيراً للتفاعل عن: {topic}. أضف 2-3 إيموجي مناسبة واقترح 4 هاشتاغات شائعة. ابدأ بسؤال أو جملة جذابة."
    else:  # Video Script (Storytelling)
        return f"اكتب سيناريو فيديو احترافي ومقنع عن: {topic}. اجعل الأسلوب قصصي وسردي، مقسماً إلى مشاهد رئيسية، مع اقتراح لقطات بصرية (B-roll) وأصوات (SFX) لكل مشهد. ركز على إثارة المشاعر."

def create_english_prompt(topic, style):
    if style == "Blog Post (Descriptive)":
        return f"Write a detailed and professional blog post about: {topic}. Focus on personal insights, vivid descriptions, and practical advice. Structure it with clear paragraphs and subheadings."
    elif style == "Social Media Post (Short & Catchy)":
        return f"Write a short, catchy, and engaging social media post about: {topic}. Include 2-3 relevant emojis and suggest 4 trending hashtags. Start with a hook question or statement."
    else:  # Video Script (Storytelling)
        return f"Write a professional, compelling video script about: {topic}. Make it emotionally engaging and story-driven, divided into key scenes, with suggested visual shots (B-roll) and sound effects (SFX) for each scene."

# --- Content Generation Function ---

@torch.no_grad() # Disable gradient calculations for inference to save memory
def generate_content(topic, style_choice, lang_choice, length_choice, creativity, detail_level, diversity_penalty):
    if tokenizer is None or model is None:
        return "⚠️ Error: Model not loaded. Please check the console for details."

    if not topic:
        return "⚠️ Please enter a topic to generate content."

    # Max length based on desired length and model's context window
    # Flan-T5 has a context window of 512, so max_length should be within this.
    if length_choice == "Short":
        max_new_tokens = 150
        min_new_tokens = 50
    elif length_choice == "Medium":
        max_new_tokens = 300
        min_new_tokens = 100
    else:  # Long
        max_new_tokens = 450 # Max for Flan-T5 effectively
        min_new_tokens = 150

    # Adjust generation parameters based on user input
    temperature = creativity # Direct mapping
    top_p = detail_level # Direct mapping, higher means more detail/diversity
    no_repeat_ngram_size = diversity_penalty # Higher means less repetition

    # Build the prompt
    if lang_choice == "Arabic":
        prompt = create_arabic_prompt(topic, style_choice)
    else:  # English
        prompt = create_english_prompt(topic, style_choice)

    # Add detail level instruction to prompt if high
    if detail_level > 0.7: # Only if user explicitly wants high detail
         prompt += " Ensure comprehensive coverage and rich descriptions."
    if creativity > 0.8:
        prompt += " Be highly creative and imaginative in your writing."

    try:
        inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True).to(device)
        
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            min_new_tokens=min_new_tokens,
            num_beams=5, # Beam search for better quality
            do_sample=True, # Enable sampling for creativity
            temperature=temperature,
            top_p=top_p,
            top_k=50, # Consider top 50 words
            no_repeat_ngram_size=no_repeat_ngram_size,
            length_penalty=1.0, # Adjust to control output length
            early_stopping=True
        )
        content = tokenizer.decode(outputs[0], skip_special_tokens=True)

        return content
    except RuntimeError as e:
        if "out of memory" in str(e):
            return "⚠️ Generation failed: Out of memory. Try a shorter length, a less complex model, or restart the application if on GPU."
        return f"⚠️ Generation failed due as runtime error: {str(e)}"
    except Exception as e:
        return f"⚠️ An unexpected error occurred during generation: {str(e)}"

# --- Gradio Interface ---

# Custom CSS for a more polished look
custom_css = """
h1, h2, h3 { color: #4B0082; } /* Dark Purple */
.gradio-container {
    background-color: #F8F0FF; /* Light Lavender */
    font-family: 'Segoe UI', sans-serif;
}
.gr-button {
    background-color: #8A2BE2; /* Blue Violet */
    color: white;
    border-radius: 10px;
    padding: 10px 20px;
    font-size: 1.1em;
}
.gr-button:hover {
    background-color: #9370DB; /* Medium Purple */
}
.gr-text-input, .gr-textarea {
    border: 1px solid #DDA0DD; /* Plum */
    border-radius: 8px;
    padding: 10px;
}
.gradio-radio input:checked + label {
    background-color: #DA70D6 !important; /* Orchid */
    color: white !important;
}
.gradio-radio label {
    border: 1px solid #DDA0DD;
    border-radius: 8px;
    padding: 8px 15px;
}
"""

with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as iface:
    gr.Markdown("# ✨ AI Content Creation Studio")
    gr.Markdown("## Generate professional blogs, social media posts, or video scripts in seconds!")
    
    with gr.Row():
        with gr.Column(scale=2):
            topic = gr.Textbox(
                label="Topic / الموضوع", 
                placeholder="e.g., The Future of AI in Healthcare / مثال: مستقبل الذكاء الاصطناعي في الرعاية الصحية",
                lines=2
            )
            
            with gr.Accordion("Advanced Settings", open=False):
                with gr.Row():
                    creativity = gr.Slider(
                        minimum=0.1, maximum=1.0, value=0.7, step=0.1, 
                        label="Creativity (Temperature)", 
                        info="Higher values lead to more creative, less predictable text. Lower values are more focused."
                    )
                    detail_level = gr.Slider(
                        minimum=0.1, maximum=1.0, value=0.9, step=0.1, 
                        label="Detail Level (Top-p Sampling)", 
                        info="Higher values allow for more diverse and detailed vocabulary. Lower values prune less likely words."
                    )
                with gr.Row():
                    diversity_penalty = gr.Slider(
                        minimum=1, maximum=5, value=2, step=1, 
                        label="Repetition Penalty (N-gram)", 
                        info="Higher values reduce the chance of repeating the same phrases or words. Set to 1 for no penalty."
                    )

        with gr.Column(scale=1):
            with gr.Group():
                style_choice = gr.Radio(
                    ["Blog Post (Descriptive)", "Social Media Post (Short & Catchy)", "Video Script (Storytelling)"],
                    label="Content Style / نوع المحتوى",
                    value="Blog Post (Descriptive)",
                    interactive=True
                )
            with gr.Group():
                lang_choice = gr.Radio(
                    ["English", "Arabic"], 
                    label="Language / اللغة",
                    value="English",
                    interactive=True
                )
            with gr.Group():
                length_choice = gr.Radio(
                    ["Short", "Medium", "Long"], 
                    label="Content Length / طول النص", 
                    value="Medium",
                    interactive=True
                )
                gr.Markdown("*(Note: 'Long' is relative to model capabilities, max ~450 words)*")

    btn = gr.Button("🚀 Generate Content", variant="primary")
    
    output = gr.Textbox(label="Generated Content", lines=20, interactive=True)
    
    # Download button logic
    def download_file(content):
        if content and not content.startswith("⚠️"): # Only provide file if content is valid
            file_path = "generated_content.txt"
            with open(file_path, "w", encoding="utf-8") as f:
                f.write(content)
            return file_path
        return None # Return None if no valid content to download

    download_button = gr.DownloadButton("⬇️ Download Content", file_path=None, interactive=False)

    # Event handlers
    btn.click(
        fn=generate_content, 
        inputs=[topic, style_choice, lang_choice, length_choice, creativity, detail_level, diversity_penalty], 
        outputs=output
    )
    
    # Enable download button only when there's valid content
    output.change(fn=download_file, inputs=[output], outputs=[download_button])

if __name__ == "__main__":
    iface.launch()