first version

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from newspaper import Article
+import nltk
+from datetime import datetime, timedelta
+import requests
+from bs4 import BeautifulSoup
+import re
+
+# Download required NLTK data
+try:
+    nltk.data.find('tokenizers/punkt')
+except LookupError:
+    nltk.download('punkt')
+
+# Initialize models and tokenizers
+def load_models():
+    # Text generation model
+    generator_model = "facebook/opt-350m"
+    generator_tokenizer = AutoTokenizer.from_pretrained(generator_model)
+    generator = AutoModelForCausalLM.from_pretrained(generator_model)
+    
+    # Sentiment analysis
+    sentiment_analyzer = pipeline(
+        "sentiment-analysis",
+        model="finiteautomata/bertweet-base-sentiment-analysis"
+    )
+    
+    # Bias detection
+    bias_detector = pipeline(
+        "text-classification",
+        model="unitary/unbiased-bertscore"
+    )
+    
+    return generator_tokenizer, generator, sentiment_analyzer, bias_detector
+
+# Function to fetch recent news
+def fetch_recent_news(query, num_articles=3):
+    base_url = "https://news.google.com/rss/search"
+    params = {
+        'q': query,
+        'hl': 'en-US',
+        'gl': 'US',
+        'ceid': 'US:en'
+    }
+    
+    try:
+        response = requests.get(base_url, params=params)
+        soup = BeautifulSoup(response.content, 'xml')
+        items = soup.find_all('item')[:num_articles]
+        
+        news_data = []
+        for item in items:
+            try:
+                article = Article(item.link.text)
+                article.download()
+                article.parse()
+                article.nlp()
+                news_data.append({
+                    'title': article.title,
+                    'summary': article.summary
+                })
+            except:
+                continue
+        
+        return news_data
+    except Exception as e:
+        return [{'title': 'Error fetching news', 'summary': str(e)}]
+
+# Generate content with ethical oversight
+def generate_content(
+    product_name,
+    product_description,
+    target_audience,
+    key_features,
+    unique_benefits,
+    platform,
+    tone,
+    generator_tokenizer,
+    generator,
+    sentiment_analyzer,
+    bias_detector
+):
+    # Format prompt based on platform
+    char_limit = 280 if platform == "Twitter" else 500
+    
+    # Get recent news for context
+    news_data = fetch_recent_news(f"{product_name} {target_audience}")
+    news_context = "\n".join([f"Recent news: {item['title']}" for item in news_data])
+    
+    # Create prompt
+    prompt = f"""
+    Product: {product_name}
+    Description: {product_description}
+    Target Audience: {target_audience}
+    Key Features: {key_features}
+    Unique Benefits: {unique_benefits}
+    Tone: {tone}
+    Platform: {platform}
+    Character Limit: {char_limit}
+    
+    {news_context}
+    
+    Create a {platform} post that highlights the product's benefits while maintaining a {tone} tone:
+    """
+    
+    # Generate initial content
+    inputs = generator_tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
+    outputs = generator.generate(
+        inputs["input_ids"],
+        max_length=char_limit,
+        num_return_sequences=3,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+    )
+    
+    generated_texts = [generator_tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
+    
+    # Filter and analyze content
+    filtered_content = []
+    for text in generated_texts:
+        # Clean up text
+        text = text.replace(prompt, "").strip()
+        
+        # Check sentiment
+        sentiment = sentiment_analyzer(text)[0]
+        
+        # Check bias
+        bias = bias_detector(text)[0]
+        
+        # Filter based on ethical considerations
+        if (
+            sentiment['label'] != 'negative' and
+            float(bias['score']) < 0.7 and  # Adjust threshold as needed
+            len(text) <= char_limit
+        ):
+            filtered_content.append({
+                'text': text,
+                'sentiment': sentiment['label'],
+                'bias_score': f"{float(bias['score']):.2f}"
+            })
+    
+    return filtered_content
+
+# Gradio interface
+def create_interface():
+    generator_tokenizer, generator, sentiment_analyzer, bias_detector = load_models()
+    
+    def process_input(
+        product_name,
+        product_description,
+        target_audience,
+        key_features,
+        unique_benefits,
+        platform,
+        tone
+    ):
+        results = generate_content(
+            product_name,
+            product_description,
+            target_audience,
+            key_features,
+            unique_benefits,
+            platform,
+            tone,
+            generator_tokenizer,
+            generator,
+            sentiment_analyzer,
+            bias_detector
+        )
+        
+        output = ""
+        for i, content in enumerate(results, 1):
+            output += f"\nVersion {i}:\n"
+            output += f"Content: {content['text']}\n"
+            output += f"Sentiment: {content['sentiment']}\n"
+            output += f"Bias Score: {content['bias_score']}\n"
+            output += "-" * 50 + "\n"
+        
+        return output
+    
+    # Create the interface
+    iface = gr.Interface(
+        fn=process_input,
+        inputs=[
+            gr.Textbox(label="Product Name"),
+            gr.Textbox(label="Product Description", lines=3),
+            gr.Textbox(label="Target Audience"),
+            gr.Textbox(label="Key Features", lines=2),
+            gr.Textbox(label="Unique Benefits", lines=2),
+            gr.Radio(
+                choices=["Twitter", "Instagram"],
+                label="Platform",
+                value="Twitter"
+            ),
+            gr.Textbox(label="Tone (e.g., professional, casual, friendly)"),
+        ],
+        outputs=gr.Textbox(label="Generated Content", lines=10),
+        title="Ethimar - AI Marketing Content Generator",
+        description="Generate ethical marketing content with AI-powered insights",
+        theme="default"
+    )
+    
+    return iface
+
+# Launch the app
+if __name__ == "__main__":
+    iface = create_interface()
+    iface.launch()

requirements.txt

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+gradio
+torch
+transformers
+newspaper3k
+nltk
+requests
+beautifulsoup4