Spaces:
Running
Running
| import torch | |
| from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration | |
| # Dummy Data: Detailed news articles | |
| news_articles = [ | |
| """Artificial Intelligence (AI) is revolutionizing industries by enhancing automation and boosting operational efficiency. | |
| Companies are leveraging AI to analyze data at scale, optimize logistics, and improve customer experiences. | |
| One notable development is the integration of AI in healthcare, where it aids in diagnosing diseases and personalizing treatment plans. | |
| Experts believe that these advancements will continue to transform how businesses operate in the coming years.""", | |
| """The field of AI has seen remarkable breakthroughs in natural language understanding, making it possible for machines to comprehend and generate human-like text. | |
| Researchers are pushing boundaries with transformer-based architectures, enabling applications like conversational agents, language translation, and content creation. | |
| These advancements are not only enhancing user interactions but also opening doors for innovative applications across various domains.""", | |
| """AI trends are shaping the future of technology and business by enabling smarter decision-making and predictive analytics. | |
| Industries such as finance, manufacturing, and retail are adopting AI-driven solutions to optimize processes and gain a competitive edge. | |
| As AI tools become more accessible, even small businesses are leveraging these technologies to scale operations and deliver better services to customers.""", | |
| ] | |
| # Load T5 Model and Tokenizer | |
| t5_tokenizer = T5Tokenizer.from_pretrained("t5-small") | |
| t5_model = T5ForConditionalGeneration.from_pretrained("t5-small") | |
| # Step 1: Input | |
| def get_user_prompt(): | |
| return input("Enter your prompt (e.g., 'Create a LinkedIn post about AI trends'): ") | |
| # Step 2: Summarization (Document Retrieval + Summarization) | |
| def summarize_articles(articles): | |
| summaries = [] | |
| for article in articles: | |
| input_text = f"summarize: {article}" | |
| inputs = t5_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True) | |
| outputs = t5_model.generate(inputs, max_length=100, min_length=50, length_penalty=2.0, num_beams=4, | |
| early_stopping=True) | |
| summary = t5_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| summaries.append(summary) | |
| return summaries | |
| # Step 3: Content Generation | |
| def generate_content(prompt, summarized_content): | |
| combined_prompt = f"{prompt}\n\nSummarized Insights:\n" + "\n".join(summarized_content) | |
| input_text = f"generate: {combined_prompt}" | |
| inputs = t5_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True) | |
| outputs = t5_model.generate(inputs, max_length=300, length_penalty=2.0, num_beams=4, early_stopping=True) | |
| generated_text = t5_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return generated_text | |
| # Step 4: Logging with Chagu (Dummy Implementation) | |
| def log_with_chagu(stage, content): | |
| print(f"\n[CHAGU LOG - {stage}]:\n{content}\n") | |
| # Step 5: Output | |
| def display_output(content): | |
| print("\nGenerated Content:") | |
| print(content) | |
| print("\nTransparency Report:") | |
| print("All transformations logged in Chagu for auditability.") | |
| # Main Workflow | |
| def main(): | |
| user_prompt = get_user_prompt() # Properly take user input | |
| log_with_chagu("Input Prompt", user_prompt) | |
| summarized_content = summarize_articles(news_articles) | |
| log_with_chagu("Summarized Articles", "\n".join(summarized_content)) | |
| final_output = generate_content(user_prompt, summarized_content) | |
| log_with_chagu("Generated Content", final_output) | |
| display_output(final_output) | |
| if __name__ == "__main__": | |
| main() | |