Upload news_content_generator.py

rag_sec/news_content_generator.py

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+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()