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| import os | |
| import gradio as gr | |
| import warnings | |
| from smolagents import CodeAgent, InferenceClientModel | |
| import json | |
| # Suppress unnecessary warnings | |
| warnings.filterwarnings("ignore") | |
| # Import our Text Summarizer Tool | |
| from summarizer_tool import TextSummarizerTool | |
| # Initialize the Text Summarizer Tool | |
| summarizer_tool = TextSummarizerTool() | |
| # Load HuggingFace token from environment variable if available | |
| hf_token = os.environ.get("HF_TOKEN") | |
| # Sample texts for quick testing | |
| sample_texts = { | |
| "News Article": """ | |
| The European Union has approved a landmark artificial intelligence law, establishing comprehensive | |
| regulations for AI systems according to their potential risks. The regulation categorizes AI into | |
| four risk levels: unacceptable risk, high risk, limited risk, and minimal risk. Systems deemed to | |
| pose unacceptable risks, such as those using subliminal manipulation or social scoring, will be | |
| banned. High-risk systems, including those used in critical infrastructure, education, employment, | |
| and law enforcement, will face strict requirements before market entry. These requirements include | |
| risk assessments, high-quality datasets, detailed documentation, human oversight, and transparency. | |
| The law aims to ensure AI systems are safe, transparent, traceable, non-discriminatory, and | |
| environmentally friendly, while fostering innovation and establishing Europe as a leader in | |
| responsible AI development. | |
| """, | |
| "Scientific Paper Abstract": """ | |
| Recent advancements in large language models (LLMs) have demonstrated remarkable capabilities | |
| across various tasks. However, these models still face challenges with reasoning, factuality, | |
| and potential biases. This paper introduces a novel framework for enhancing LLM performance | |
| through a multi-stage processing pipeline that integrates retrieval-augmented generation, | |
| self-reflection mechanisms, and external knowledge verification. Our approach, which we call | |
| RACER (Retrieval-Augmented Chain-of-thought Enhanced Reasoning), demonstrates significant | |
| improvements across benchmarks testing reasoning (GSM8K, +12.3%), factuality (FACTOR, +17.8%), | |
| and reduced bias (BBQ, -24.5%) compared to base models. Additionally, we show that RACER is | |
| complementary to existing techniques like chain-of-thought prompting and can be applied to | |
| various model architectures with minimal computational overhead. Through extensive ablation | |
| studies, we identify the contribution of each component and provide insights for efficient | |
| implementation in real-world applications. | |
| """, | |
| "Business Report": """ | |
| Q1 Financial Performance Summary: The company achieved significant growth in the first quarter, | |
| with revenue reaching $78.5 million, a 24% increase compared to the same period last year. This | |
| growth was primarily driven by our expanded product portfolio and increased market penetration in | |
| European and Asian markets. Our flagship product line saw sales increase by 32%, while our new | |
| service offerings contributed $12.3 million in revenue. Gross margin improved to 62.8% from 58.4% | |
| in the previous year, reflecting our successful cost optimization initiatives and economies of scale. | |
| Operating expenses were $28.7 million, up 15% year-over-year, primarily due to increased R&D | |
| investments and marketing campaigns for new product launches. Despite these investments, operating | |
| profit grew by 42% to $20.5 million, representing a 26.1% operating margin. Our customer base expanded | |
| by 15%, with particularly strong growth in the enterprise segment. Looking ahead, we maintain our | |
| full-year guidance of $320-340 million in revenue and anticipate continued margin improvement as we | |
| scale operations. | |
| """ | |
| } | |
| # Function to directly use the summarizer tool | |
| def summarize_text(text, model, max_length, min_length, style): | |
| try: | |
| # Convert max_length and min_length to integers | |
| max_length = int(max_length) if max_length else None | |
| min_length = int(min_length) if min_length else None | |
| # Call the summarizer tool | |
| result = summarizer_tool( | |
| text=text, | |
| model=model, | |
| max_length=max_length, | |
| min_length=min_length, | |
| style=style | |
| ) | |
| return result | |
| except Exception as e: | |
| return f"Error summarizing text: {str(e)}" | |
| # Function to use the summarizer with an agent (if token available) | |
| def agent_summarize(text, instruction, temperature=0.7): | |
| if not hf_token: | |
| return "Agent summarization requires a HuggingFace API token. Please set the HF_TOKEN environment variable." | |
| if not text or not instruction: | |
| return "Please provide both text and instructions." | |
| try: | |
| # Initialize model for agent | |
| model = InferenceClientModel( | |
| model_id="mistralai/Mistral-7B-Instruct-v0.2", | |
| token=hf_token, | |
| temperature=float(temperature) | |
| ) | |
| # Create the agent with our summarizer tool | |
| agent = CodeAgent(tools=[summarizer_tool], model=model) | |
| # Format the prompt with the instruction and text | |
| prompt = f"Instruction: {instruction}\n\nText to summarize: {text}" | |
| # Run the agent | |
| result = agent.run(prompt) | |
| return result | |
| except Exception as e: | |
| return f"Error with agent summarization: {str(e)}" | |
| # Create Gradio interface | |
| with gr.Blocks(title="Advanced Text Summarizer") as demo: | |
| gr.Markdown("# 📝 Advanced Text Summarizer") | |
| gr.Markdown("Summarize text using different models and styles, with optional agent assistance.") | |
| with gr.Tabs(): | |
| # Direct summarization tab | |
| with gr.Tab("Direct Summarization"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Input section | |
| direct_text_input = gr.Textbox( | |
| label="Text to Summarize", | |
| placeholder="Enter text to summarize...", | |
| lines=10 | |
| ) | |
| # Sample texts dropdown | |
| sample_dropdown = gr.Dropdown( | |
| choices=list(sample_texts.keys()), | |
| label="Or Select a Sample Text" | |
| ) | |
| # Configuration options | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_dropdown = gr.Dropdown( | |
| choices=list(summarizer_tool.available_models.keys()), | |
| value="facebook/bart-large-cnn", | |
| label="Summarization Model" | |
| ) | |
| style_dropdown = gr.Dropdown( | |
| choices=["concise", "detailed", "bullet_points"], | |
| value="concise", | |
| label="Summary Style" | |
| ) | |
| with gr.Column(): | |
| max_length_slider = gr.Slider( | |
| minimum=50, | |
| maximum=250, | |
| value=130, | |
| step=10, | |
| label="Maximum Summary Length" | |
| ) | |
| min_length_slider = gr.Slider( | |
| minimum=10, | |
| maximum=100, | |
| value=30, | |
| step=5, | |
| label="Minimum Summary Length" | |
| ) | |
| direct_summarize_button = gr.Button("Summarize Text") | |
| with gr.Column(): | |
| # Output section | |
| direct_output = gr.Textbox(label="Summary", lines=12) | |
| # Agent-assisted summarization tab | |
| with gr.Tab("Agent-Assisted Summarization"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Input section | |
| agent_text_input = gr.Textbox( | |
| label="Text to Summarize", | |
| placeholder="Enter text to summarize...", | |
| lines=10 | |
| ) | |
| # Agent instruction | |
| instruction_input = gr.Textbox( | |
| label="Instructions for the Agent", | |
| placeholder="E.g., 'Summarize this text and highlight the three most important points'", | |
| lines=2, | |
| value="Summarize this text in a professional tone, highlighting key information." | |
| ) | |
| # Sample instructions | |
| instruction_examples = gr.Dropdown( | |
| choices=[ | |
| "Summarize this text in a professional tone, highlighting key information.", | |
| "Create a very concise summary focusing only on actionable items.", | |
| "Summarize this for a high school student, explaining complex terms.", | |
| "Extract the main argument and supporting evidence from this text.", | |
| "Create a summary that focuses on financial implications mentioned in the text." | |
| ], | |
| label="Or Select Example Instructions" | |
| ) | |
| # Temperature setting | |
| temperature_slider = gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.7, | |
| step=0.1, | |
| label="Agent Temperature (creativity)" | |
| ) | |
| agent_summarize_button = gr.Button("Use Agent to Summarize") | |
| with gr.Column(): | |
| # Output section | |
| agent_output = gr.Textbox(label="Agent Response", lines=15) | |
| # Set up event handlers | |
| def load_sample(sample_name): | |
| return sample_texts.get(sample_name, "") | |
| def load_instruction(instruction): | |
| return instruction | |
| sample_dropdown.change( | |
| load_sample, | |
| inputs=sample_dropdown, | |
| outputs=direct_text_input | |
| ) | |
| instruction_examples.change( | |
| load_instruction, | |
| inputs=instruction_examples, | |
| outputs=instruction_input | |
| ) | |
| direct_summarize_button.click( | |
| summarize_text, | |
| inputs=[direct_text_input, model_dropdown, max_length_slider, min_length_slider, style_dropdown], | |
| outputs=direct_output | |
| ) | |
| agent_summarize_button.click( | |
| agent_summarize, | |
| inputs=[agent_text_input, instruction_input, temperature_slider], | |
| outputs=agent_output | |
| ) | |
| # Also allow using sample text for agent tab | |
| sample_dropdown.change( | |
| load_sample, | |
| inputs=sample_dropdown, | |
| outputs=agent_text_input | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| demo.launch() |