Create app.py

app.py

@@ -0,0 +1,235 @@
+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()