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requirements.txt

@@ -1,239 +1,7 @@
-import gradio as gr
-from transformers import pipeline
-import torch
-import os
-
-# Initialize the text generation pipeline
-generator = None
-
-def initialize_model():
-    global generator
-    try:
-        # Check available memory and choose model accordingly
-        import psutil
-        available_memory_gb = psutil.virtual_memory().available / (1024**3)
-        
-        if available_memory_gb > 20:  # If we have enough memory, try 7B
-            device = 0 if torch.cuda.is_available() else -1
-            generator = pipeline(
-                "text-generation",
-                model="Qwen/Qwen2.5-7B-Instruct",
-                device=device,
-                torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-                trust_remote_code=True
-            )
-            return f"Qwen 2.5-7B-Instruct loaded successfully on {'GPU' if device == 0 else 'CPU'}!"
-        else:
-            raise Exception("Insufficient memory for 7B model, falling back to 1.5B")
-            
-    except Exception as e:
-        # Fallback to 1.5B model for free tier compatibility
-        try:
-            generator = pipeline(
-                "text-generation", 
-                model="Qwen/Qwen2.5-1.5B-Instruct",
-                device=-1,
-                torch_dtype=torch.float32,
-                trust_remote_code=True
-            )
-            return "Qwen 2.5-1.5B-Instruct loaded successfully on CPU (optimized for free tier)!"
-        except Exception as e2:
-            # Final fallback to a very reliable model
-            try:
-                generator = pipeline(
-                    "text-generation", 
-                    model="microsoft/DialoGPT-large",
-                    device=-1,
-                    torch_dtype=torch.float32
-                )
-                return "DialoGPT-large loaded as final fallback!"
-            except Exception as e3:
-                return f"All models failed: 7B: {str(e)}, 1.5B: {str(e2)}, DialoGPT: {str(e3)}"
-
-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}.
-
-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
-
-Please write the complete one-page document now.
-<|im_end|>
-<|im_start|>assistant
-# {topic}
-
-## Executive Summary
-
-"""
-    
-    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)
-
-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}
-
-## 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.
-
-## Key Points
-
-{chr(10).join([f"• {point.strip()}" for point in key_points.split(',') if point.strip()])}
-
-## 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()}.
-
-## 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.
-
-## Benefits & Opportunities
-- Enhanced understanding of core concepts
-- Improved decision-making capabilities  
-- Strategic advantages for implementation
-- Clear actionable insights
-
-## 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 with Qwen 2.5-7B...")
-    print("📥 Loading AI model...")
-    initialize_model()
-    print("✅ Model loaded! Launching interface...")
-    
-    demo = create_interface()
-    demo.launch()
+torch
+transformers
+gradio
+huggingface_hub
+tokenizers
+accelerate
+psutil