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Running
on
Zero
| # Import necessary libraries | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| import spaces | |
| tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Instruct") | |
| model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") | |
| def generate_text(prompt, max_length, temperature, category): | |
| category_prompts = { | |
| "Elder-Friendly": "Explain this concept step-by-step in very simple and clear terms, avoiding any technical jargon or complex words, so that seniors can easily understand: ", | |
| "Kid-Friendly": "Break down this concept into a fun, story-like explanation using simple words and examples that children can relate to and enjoy: ", | |
| "Teen-Friendly": "Make this concept relatable, engaging, and a bit entertaining for teenagers by using examples from pop culture, games, or their daily lives: ", | |
| "Beginner Coders": "Teach this concept as if you are explaining it to someone completely new to programming, using clear analogies and real-world coding examples: ", | |
| "Non-Techies": "Simplify this concept into very clear and plain language, avoiding technical terms while using examples that are easy for a non-technical audience to relate to: ", | |
| "Visual Thinkers": "Use descriptive analogies, mental imagery, and comparisons to help visualize this concept clearly in an easy-to-grasp manner: ", | |
| "Busy Professionals": "Summarize this concept briefly and concisely, focusing only on the essential details to save time, while keeping it professional and clear: ", | |
| "Curious Learners": "Explain this concept in detail, diving into its meaning, examples, and practical relevance, while maintaining clarity and flow: ", | |
| "Tech Enthusiasts": "Provide an insightful and technical explanation of this concept, including its relevance, practical applications, and deeper implications in the tech world: ", | |
| "Educators": "Frame this concept as a teaching guide, providing step-by-step clarity and examples that would be helpful for explaining it to a classroom or audience: ", | |
| "Business Leaders": "Explain this concept from a strategic perspective, focusing on its business relevance, use cases, and real-world value in a professional setting: ", | |
| "Problem Solvers": "Describe this concept with a problem-solving mindset, focusing on practical applications, benefits, and how it can be applied to resolve challenges: " | |
| } | |
| # Prepend the category-specific prompt | |
| category_prompt = category_prompts.get(category, "") | |
| full_prompt = category_prompt + prompt | |
| inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device) | |
| outputs = model.generate( | |
| **inputs, | |
| max_length=max_length, | |
| temperature=temperature, | |
| do_sample=True | |
| ) | |
| generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| generated_text = generated_text.replace(category_prompt, "") | |
| print(generated_text) | |
| return generated_text | |
| # Gradio app interface with input and output components | |
| with gr.Blocks() as demo: | |
| gr.Markdown("#Tech Explainer\nEnter a concept, select a category, and Falcon 3-7B-Instruct will generate a simplified explanation!") | |
| with gr.Row(): | |
| prompt_input = gr.Textbox(label="Enter your concept here", lines=3, placeholder="Type something...") | |
| with gr.Row(): | |
| category_input = gr.Dropdown([ | |
| "Elder-Friendly", "Kid-Friendly", "Teen-Friendly", | |
| "Beginner Coders", "Non-Techies", "Visual Thinkers", | |
| "Busy Professionals", "Curious Learners", | |
| "Tech Enthusiasts", "Educators", | |
| "Business Leaders", "Problem Solvers" | |
| ], label="Select Audience Category", value="Elder-Friendly") | |
| with gr.Row(): | |
| max_length = gr.Slider(50, 500, value=150, step=10, label="Max Length") | |
| temperature = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature") | |
| with gr.Row(): | |
| generate_button = gr.Button("Generate Explanation") | |
| with gr.Row(): | |
| gr.Markdown("Generated Explanation") | |
| with gr.Row(): | |
| output = gr.Markdown("Output will generate here") | |
| generate_button.click(generate_text, inputs=[prompt_input, max_length, temperature, category_input], outputs=output) | |
| demo.launch() | |