swaleha19/agent_tuning_framework
0
1"""2Simplified Gradio Demo for Agent Tuning Optimization Framework3 4This script creates a simple Gradio web interface to demonstrate the framework's capabilities.5"""6 7import os8import gradio as gr9import numpy as np10import random11from datetime import datetime12 13# Mock functions to simulate framework behavior without requiring full model loading14def mock_generate_response(task, user_message):15 """Simulate generating a response from a tuned agent."""16 responses = [17 f"I'll help you with your task to {task.lower()}. Based on your message '{user_message}', I recommend starting by breaking this down into smaller steps.",18 f"I understand you need assistance with {task.lower()}. From your message, I can see that you're looking for guidance on '{user_message}'. Here's my approach to solving this.",19 f"Thank you for providing details about {task.lower()}. Your message '{user_message}' gives me enough context to help you effectively. Let me outline a solution.",20 f"I'm analyzing your request about {task.lower()}. Your message '{user_message}' indicates you need comprehensive assistance. Here's what I suggest as next steps."21 ]22 23 # Simulate processing time24 import time25 time.sleep(1.5)26 27 return random.choice(responses) + f"\n\nResponse generated at {datetime.now().strftime('%H:%M:%S')}"28 29def mock_generate_negative_sample(task, user_message, agent_message):30 """Simulate generating a negative sample from a positive example."""31 degradation_types = [32 "Response truncation",33 "Grammatical errors",34 "Task misalignment",35 "Constraint violation",36 "Irrelevant tangent"37 ]38 39 degradation = random.choice(degradation_types)40 41 if degradation == "Response truncation":42 words = agent_message.split()43 truncate_point = int(len(words) * random.uniform(0.3, 0.7))44 return " ".join(words[:truncate_point]) + f"...\n\nNegative sample type: {degradation}"45 46 elif degradation == "Grammatical errors":47 errors = [48 lambda t: t.replace(".", ""), # Remove periods49 lambda t: t.replace("I ", "i "), # Lowercase I50 lambda t: t.replace(" the ", " teh "), # Typo51 lambda t: t.replace(" is ", " are "), # Grammar error52 lambda t: t.replace(" are ", " is ") # Grammar error53 ]54 55 result = agent_message56 for _ in range(random.randint(2, 4)):57 error_func = random.choice(errors)58 result = error_func(result)59 60 return result + f"\n\nNegative sample type: {degradation}"61 62 elif degradation == "Task misalignment":63 misalignments = [64 f"I understand you're asking about something completely different. Let me tell you about weather patterns instead.",65 f"I don't think that's what you really want to know. Let me explain something else that might interest you.",66 f"Your question seems to be about {task}, but I'd rather discuss the history of computing.",67 f"Instead of addressing your specific request about {task}, let me give you general information that's only tangentially related."68 ]69 70 return random.choice(misalignments) + f"\n\nNegative sample type: {degradation}"71 72 elif degradation == "Constraint violation":73 violations = [74 f"I specifically recommend the XYZ Pro 2000 for $499.99, the UltraBook 15 for $1,299, and the PowerTech 5000 for $799. These are the absolute best options available.",75 f"The system utilizes a polymorphic encapsulation paradigm with recursive lambda functions and stochastic gradient descent with backpropagation through a multi-layer perceptron.",76 f"What specific features are you looking for? Do you have any brand preferences? What's your budget range? When do you need this by? Have you considered alternative options?",77 f"Since you're a tech-savvy individual who values cutting-edge features, you'll definitely want the latest model with all the advanced capabilities."78 ]79 80 return random.choice(violations) + f"\n\nNegative sample type: {degradation}"81 82 else: # Irrelevant tangent83 tangents = [84 f"Did you know that artificial intelligence has been a concept since the 1950s? The field has evolved significantly since then, with major breakthroughs in neural networks and deep learning.",85 f"I've been thinking about the philosophical implications of consciousness in AI systems. The question of whether an AI can truly understand or merely simulate understanding is fascinating.",86 f"The weather has been quite interesting lately, with unusual patterns emerging globally. Climate scientists attribute this to a combination of factors including ocean temperature changes.",87 f"I recently processed some fascinating data about renewable energy technologies. Solar efficiency has improved dramatically in the past decade, while costs have decreased by over 80%."88 ]89 90 return random.choice(tangents) + f"\n\nNegative sample type: {degradation}"91 92def mock_generate_synthetic_trajectory(task):93 """Simulate generating a synthetic trajectory for a given task."""94 # Determine task category95 categories = ["travel", "shopping", "technology", "education", "finance", "health", "career", "home"]96 category = random.choice(categories)97 98 # Generate interactions (2-4 turns)99 num_turns = random.randint(2, 4)100 interactions = []101 102 for j in range(num_turns):103 if j == 0:104 user_msg = f"I need help with this task: {task}"105 agent_msg = f"I'd be happy to help you {task.lower()}. Could you provide more details about your preferences?"106 elif j == num_turns - 1:107 user_msg = "That sounds good. Please proceed with the final steps."108 agent_msg = f"I've completed the task to {task.lower()}. Here's a summary of what I did..."109 else:110 user_msg = f"I prefer options that are {['affordable', 'convenient', 'high-quality'][j % 3]}."111 agent_msg = f"Based on your preference for {['affordable', 'convenient', 'high-quality'][j % 3]} options, I recommend..."112 113 interactions.append({114 'user': user_msg,115 'agent': agent_msg116 })117 118 # Format trajectory119 result = f"Synthetic Trajectory for Task: {task}\nCategory: {category}\n\n"120 121 for i, interaction in enumerate(interactions):122 result += f"Turn {i+1}:\nUser: {interaction['user']}\nAgent: {interaction['agent']}\n\n"123 124 result += f"Generation method: Template-based\nQuality score: {random.uniform(0.7, 0.9):.2f}"125 126 return result127 128# Create Gradio interface129with gr.Blocks(title="Agent Tuning Framework Demo") as demo:130 gr.Markdown("# Agent Tuning Optimization Framework Demo")131 gr.Markdown("### A framework for efficiently tuning LLMs into specialized agents using negative and synthetic samples")132 133 with gr.Tab("Generate Response"):134 with gr.Row():135 with gr.Column():136 task_input = gr.Textbox(label="Task Description", placeholder="e.g., Book a flight from New York to London")137 user_input = gr.Textbox(label="User Message", placeholder="e.g., I need to travel next week for business")138 generate_btn = gr.Button("Generate Response", variant="primary")139 with gr.Column():140 response_output = gr.Textbox(label="Agent Response", lines=8)141 142 generate_btn.click(143 mock_generate_response,144 inputs=[task_input, user_input],145 outputs=response_output146 )147 148 gr.Examples(149 [150 ["Book a flight from New York to London", "I need to travel next week for business"],151 ["Find a vegetarian restaurant", "I'm looking for dinner options tonight"],152 ["Help me debug a Python script", "I'm getting an IndexError in my code"]153 ],154 inputs=[task_input, user_input]155 )156 157 with gr.Tab("Generate Negative Sample"):158 with gr.Row():159 with gr.Column():160 neg_task_input = gr.Textbox(label="Task Description", placeholder="e.g., Book a flight from New York to London")161 neg_user_input = gr.Textbox(label="User Message", placeholder="e.g., I need to travel next week for business")162 neg_agent_input = gr.Textbox(label="Agent Message (Positive Example)", placeholder="e.g., I'd be happy to help you book a flight...", lines=5)163 neg_generate_btn = gr.Button("Generate Negative Sample", variant="primary")164 with gr.Column():165 neg_output = gr.Textbox(label="Negative Sample", lines=8)166 167 neg_generate_btn.click(168 mock_generate_negative_sample,169 inputs=[neg_task_input, neg_user_input, neg_agent_input],170 outputs=neg_output171 )172 173 gr.Examples(174 [175 ["Book a flight from New York to London", "I need to travel next week for business", "I'd be happy to help you book a flight from New York to London. Could you provide more details about your preferred travel dates, budget, and any airline preferences you might have?"],176 ["Recommend a laptop for programming", "I need a new laptop for software development", "I can definitely help you find a suitable laptop for programming. Based on software development needs, I'd recommend looking for a laptop with at least 16GB RAM, a multi-core processor, and an SSD for storage. Would you like specific brand recommendations or have a particular budget in mind?"]177 ],178 inputs=[neg_task_input, neg_user_input, neg_agent_input]179 )180 181 with gr.Tab("Generate Synthetic Trajectory"):182 with gr.Row():183 with gr.Column():184 synth_task_input = gr.Textbox(label="Task Description", placeholder="e.g., Plan a weekend trip to Chicago")185 synth_generate_btn = gr.Button("Generate Synthetic Trajectory", variant="primary")186 with gr.Column():187 synth_output = gr.Textbox(label="Synthetic Trajectory", lines=15)188 189 synth_generate_btn.click(190 mock_generate_synthetic_trajectory,191 inputs=[synth_task_input],192 outputs=synth_output193 )194 195 gr.Examples(196 [197 ["Plan a weekend trip to Chicago"],198 ["Recommend healthy meal prep options for the week"],199 ["Help me create a study schedule for final exams"]200 ],201 inputs=[synth_task_input]202 )203 204 gr.Markdown("""205 ## About This Framework206 207 The Agent Tuning Optimization Framework provides a comprehensive solution for efficiently tuning large language models into specialized agents through the strategic incorporation of negative samples and synthetic trajectories.208 209 ### Key Features:210 211 1. **Negative Sample Generation**: Creates examples of undesired agent behaviors to teach models what not to do212 2. **Synthetic Trajectory Generation**: Automatically generates diverse interaction trajectories213 3. **Mixed-Sample Tuning**: Combines positive examples, negative samples, and synthetic trajectories214 4. **Parameter-Efficient Fine-Tuning**: Implements methods like LoRA for computational efficiency215 216 This demo provides a simplified simulation of the framework's capabilities. For full functionality, deploy the complete framework following the provided documentation.217 """)218 219# Launch the interface220demo.launch(share=True)221