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README.md

@@ -1,52 +1,232 @@
-# 📄 AI One-Pager Generator
+import gradio as gr
+from transformers import pipeline
+import torch
+import os
 
-An intelligent Gradio application that generates professional one-page documents on any topic using Hugging Face transformers.
+# Initialize the text generation pipeline
+generator = None
 
-## Features
+def initialize_model():
+    global generator
+    try:
+        # Use Qwen 2.5 - state-of-the-art 2024/2025 model for document generation
+        generator = pipeline(
+            "text-generation",
+            model="Qwen/Qwen2.5-1.5B-Instruct",  # Latest Qwen model, perfect size for Spaces
+            device=-1,  # CPU only for compatibility
+            torch_dtype=torch.float32,
+            trust_remote_code=True
+        )
+        return "Qwen 2.5-1.5B-Instruct loaded successfully!"
+    except Exception as e:
+        # Fallback to smaller Qwen model
+        try:
+            generator = pipeline(
+                "text-generation",
+                model="Qwen/Qwen2.5-0.5B-Instruct", 
+                device=-1,
+                torch_dtype=torch.float32,
+                trust_remote_code=True
+            )
+            return "Fallback model (Qwen 2.5-0.5B-Instruct) loaded successfully!"
+        except Exception as e2:
+            # Final fallback to Phi-3 mini
+            try:
+                generator = pipeline(
+                    "text-generation", 
+                    model="microsoft/Phi-3-mini-4k-instruct",
+                    device=-1,
+                    torch_dtype=torch.float32,
+                    trust_remote_code=True
+                )
+                return "Final fallback model (Phi-3-mini) loaded successfully!"
+            except Exception as e3:
+                return f"All modern models failed: Qwen: {str(e)}, Qwen-small: {str(e2)}, Phi-3: {str(e3)}"
 
-- **Topic-based Generation**: Create one-pagers on any subject
-- **Customizable Parameters**: 
-  - Target audience specification
-  - Tone selection (Professional, Casual, Academic, Persuasive, Informative)
-  - Length control (Short, Medium, Long)
-  - Key points input
-- **Professional Formatting**: Structured output with title, executive summary, key points, and conclusion
-- **Easy to Use**: Simple web interface powered by Gradio
+def generate_onepager(topic, target_audience, key_points, tone, length):
+    if generator is None:
+        return "Error: Model not initialized. Please wait for the model to load."
+    
+    # Create a structured prompt for one-pager generation
+    length_tokens = {"Short": 200, "Medium": 400, "Long": 600}
+    max_tokens = length_tokens.get(length, 400)
+    
+    # Create an optimized prompt for Qwen 2.5 instruction format
+    prompt = f"""<|im_start|>system
+You are a professional document writer specializing in creating concise, well-structured one-page business documents.
+<|im_end|>
+<|im_start|>user
+Create a professional one-page document about "{topic}" targeted at {target_audience}.
 
-## Installation
+Requirements:
+- Tone: {tone.lower()}
+- Key points to include: {key_points}
+- Length: {length}
+- Format: Use clear headers and bullet points
+- Structure: Title, Executive Summary, Key Points, Benefits, Recommendations, Conclusion
 
-1. Clone this repository
-2. Install dependencies:
-```bash
-pip install -r requirements.txt
-```
+Please write the complete one-page document now.
+<|im_end|>
+<|im_start|>assistant
+# {topic}
 
-3. Run the application:
-```bash
-python app.py
-```
+## Executive Summary
 
-## Usage
+"""
+    
+    try:
+        # Generate the one-pager
+        result = generator(
+            prompt,
+            max_length=len(prompt.split()) + max_tokens,
+            num_return_sequences=1,
+            temperature=0.8,
+            do_sample=True,
+            pad_token_id=generator.tokenizer.eos_token_id,
+            eos_token_id=generator.tokenizer.eos_token_id,
+            repetition_penalty=1.1
+        )
+        
+        # Extract the generated text
+        generated_text = result[0]['generated_text']
+        
+        # Clean up the output
+        onepager = generated_text.replace(prompt, "").strip()
+        
+        # If output is too short, provide a structured fallback
+        if len(onepager) < 50:
+            onepager = create_structured_onepager(topic, target_audience, key_points, tone)
+        
+        return onepager
+        
+    except Exception as e:
+        # Fallback to structured template
+        return create_structured_onepager(topic, target_audience, key_points, tone)
 
-1. Enter your topic in the "Topic" field
-2. Specify your target audience
-3. List key points you want covered
-4. Select the appropriate tone and length
-5. Click "Generate One-Pager"
-6. Copy and use your generated document!
+def create_structured_onepager(topic, target_audience, key_points, tone):
+    """Create a structured one-pager when AI generation fails"""
+    
+    tone_styles = {
+        "Professional": "formal and business-oriented",
+        "Casual": "friendly and approachable",
+        "Academic": "scholarly and research-focused", 
+        "Persuasive": "compelling and action-oriented",
+        "Informative": "clear and educational"
+    }
+    
+    style_desc = tone_styles.get(tone, "professional")
+    
+    template = f"""# {topic}
 
-## Deployment to Hugging Face Spaces
+## Executive Summary
+This document provides a comprehensive overview of {topic.lower()} tailored for {target_audience.lower()}. The content is presented in a {style_desc} manner to ensure maximum impact and understanding.
 
-This app is ready to be deployed to Hugging Face Spaces:
+## Key Points
 
-1. Create a new Space on Hugging Face
-2. Upload these files to your Space
-3. Your app will be automatically deployed!
+{chr(10).join([f"• {point.strip()}" for point in key_points.split(',') if point.strip()])}
 
-## Model Information
+## Background
+{topic} represents an important area that requires careful consideration and strategic thinking. Understanding the core concepts and implications is essential for {target_audience.lower()}.
 
-This application uses Microsoft's DialoGPT-large model for text generation, optimized for document creation tasks.
+## Main Content
+The fundamental aspects of {topic.lower()} encompass several critical areas that directly impact stakeholders. These elements work together to create a comprehensive framework for understanding and implementation.
 
-## License
+## Benefits & Opportunities
+- Enhanced understanding of core concepts
+- Improved decision-making capabilities  
+- Strategic advantages for implementation
+- Clear actionable insights
 
-MIT License
+## Recommendations
+1. Begin with thorough analysis of current situation
+2. Develop comprehensive implementation strategy
+3. Monitor progress and adjust approach as needed
+4. Measure results and iterate for continuous improvement
+
+## Conclusion
+{topic} offers significant opportunities for {target_audience.lower()} when approached strategically. The key points outlined above provide a solid foundation for moving forward with confidence and clarity.
+
+---
+*This one-pager was generated to provide quick, actionable insights on {topic.lower()}.*"""
+    
+    return template
+
+# Create the Gradio interface
+def create_interface():
+    with gr.Blocks(title="One-Pager Generator", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 📄 AI One-Pager Generator")
+        gr.Markdown("Generate professional one-page documents on any topic using AI!")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                topic_input = gr.Textbox(
+                    label="Topic",
+                    placeholder="e.g., Digital Marketing Strategy, Climate Change Solutions, etc.",
+                    lines=2,
+                    value="Artificial Intelligence in Healthcare"
+                )
+                
+                audience_input = gr.Textbox(
+                    label="Target Audience",
+                    placeholder="e.g., Business executives, Students, General public, etc.",
+                    lines=1,
+                    value="Healthcare professionals"
+                )
+                
+                keypoints_input = gr.Textbox(
+                    label="Key Points to Cover",
+                    placeholder="Enter main points separated by commas",
+                    lines=4,
+                    value="Machine learning applications, Data privacy, Cost-effectiveness, Implementation challenges"
+                )
+                
+                tone_dropdown = gr.Dropdown(
+                    choices=["Professional", "Casual", "Academic", "Persuasive", "Informative"],
+                    label="Tone",
+                    value="Professional"
+                )
+                
+                length_dropdown = gr.Dropdown(
+                    choices=["Short", "Medium", "Long"],
+                    label="Length",
+                    value="Medium"
+                )
+                
+                generate_btn = gr.Button("🚀 Generate One-Pager", variant="primary")
+                
+            with gr.Column(scale=2):
+                output_text = gr.Textbox(
+                    label="Generated One-Pager",
+                    lines=25,
+                    max_lines=35,
+                    show_copy_button=True,
+                    placeholder="Your generated one-pager will appear here..."
+                )
+        
+        with gr.Row():
+            gr.Markdown("""
+            ### 💡 Tips for Best Results:
+            - **Be specific** with your topic for more targeted content
+            - **Include 3-5 key points** separated by commas
+            - **Choose the right tone** for your intended audience  
+            - **Use descriptive audience** details (e.g., "C-level executives" vs "executives")
+            """)
+        
+        # Connect the generate button to the function
+        generate_btn.click(
+            fn=generate_onepager,
+            inputs=[topic_input, audience_input, keypoints_input, tone_dropdown, length_dropdown],
+            outputs=output_text
+        )
+        
+    return demo
+
+# Initialize model and launch
+if __name__ == "__main__":
+    print("🚀 Starting One-Pager Generator...")
+    print("📥 Loading AI model...")
+    initialize_model()
+    print("✅ Model loaded! Launching interface...")
+    
+    demo = create_interface()
+    demo.launch()