changed ethical API

app.py

@@ -5,7 +5,6 @@ import nltk
 from datetime import datetime, timedelta
 import requests
 from bs4 import BeautifulSoup
-import json
 
 # Download required NLTK data
 try:
@@ -15,28 +14,31 @@ except LookupError:
 
 # 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
+    try:
+        # 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"
+        )
+        
+        # Content safety checker
+        content_checker = pipeline(
+            "text-classification",
+            model="facebook/roberta-hate-speech-dynabench-r4-target"
+        )
+        
+        return generator_tokenizer, generator, sentiment_analyzer, content_checker
+    except Exception as e:
+        print(f"Error loading models: {str(e)}")
+        raise
 
 # Simplified news fetching function
 def fetch_recent_news(query, num_articles=3):
-    # Using Google News RSS feed
     base_url = "https://news.google.com/rss/search"
     params = {
         'q': query,
@@ -76,7 +78,7 @@ def generate_content(
     generator_tokenizer,
     generator,
     sentiment_analyzer,
-    bias_detector
+    content_checker
 ):
     # Format prompt based on platform
     char_limit = 280 if platform == "Twitter" else 500
@@ -87,66 +89,71 @@ def generate_content(
     
     # 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}
+    Create a {platform} post with these requirements:
+    - Product Name: {product_name}
+    - Description: {product_description}
+    - Target Audience: {target_audience}
+    - Key Features: {key_features}
+    - Unique Benefits: {unique_benefits}
+    - Tone: {tone}
+    - Maximum Length: {char_limit} characters
     
+    Recent Market Context:
     {news_context}
     
-    Create a {platform} post that highlights the product's benefits while maintaining a {tone} tone:
+    Generate a compelling {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()
+    try:
+        # 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 + len(prompt),
+            num_return_sequences=3,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True,
+        )
         
-        # Skip if text is too short
-        if len(text) < 10:
-            continue
-            
-        # Check sentiment
-        sentiment = sentiment_analyzer(text)[0]
+        generated_texts = [generator_tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
         
-        # Check bias
-        bias = bias_detector(text)[0]
+        # Filter and analyze content
+        filtered_content = []
+        for text in generated_texts:
+            # Clean up text by removing the prompt
+            text = text.replace(prompt, "").strip()
+            
+            # Skip if text is too short or too long
+            if len(text) < 10 or len(text) > char_limit:
+                continue
+                
+            # Check sentiment
+            sentiment = sentiment_analyzer(text)[0]
+            
+            # Check content safety
+            safety_check = content_checker(text)[0]
+            
+            # Filter based on ethical considerations
+            if (
+                sentiment['label'] != 'negative' and
+                safety_check['label'] == 'not_hate' and
+                len(text) <= char_limit
+            ):
+                filtered_content.append({
+                    'text': text,
+                    'sentiment': sentiment['label'],
+                    'safety_score': f"{float(safety_check['score']):.2f}"
+                })
         
-        # 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
+        return filtered_content
+    except Exception as e:
+        print(f"Error generating content: {str(e)}")
+        return []
 
 # Gradio interface
 def create_interface():
-    generator_tokenizer, generator, sentiment_analyzer, bias_detector = load_models()
+    generator_tokenizer, generator, sentiment_analyzer, content_checker = load_models()
     
     def process_input(
         product_name,
@@ -157,32 +164,35 @@ def create_interface():
         platform,
         tone
     ):
-        results = generate_content(
-            product_name,
-            product_description,
-            target_audience,
-            key_features,
-            unique_benefits,
-            platform,
-            tone,
-            generator_tokenizer,
-            generator,
-            sentiment_analyzer,
-            bias_detector
-        )
-        
-        if not results:
-            return "No suitable content generated. Please try again with different parameters."
-        
-        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
+        try:
+            results = generate_content(
+                product_name,
+                product_description,
+                target_audience,
+                key_features,
+                unique_benefits,
+                platform,
+                tone,
+                generator_tokenizer,
+                generator,
+                sentiment_analyzer,
+                content_checker
+            )
+            
+            if not results:
+                return "No suitable content generated. Please try again with different parameters."
+            
+            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"Safety Score: {content['safety_score']}\n"
+                output += "-" * 50 + "\n"
+            
+            return output
+        except Exception as e:
+            return f"An error occurred: {str(e)}"
     
     # Create the interface
     iface = gr.Interface(
@@ -203,7 +213,18 @@ def create_interface():
         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"
+        theme="default",
+        examples=[
+            [
+                "EcoBottle",
+                "Sustainable water bottle made from recycled ocean plastic",
+                "Environmentally conscious young professionals",
+                "100% recycled materials, Insulated design, Leak-proof",
+                "Helps clean oceans, Keeps drinks cold for 24 hours",
+                "Twitter",
+                "professional"
+            ]
+        ]
     )
     
     return iface

requirements.txt

@@ -1,7 +1,6 @@
 gradio
 torch
 transformers
-newsapi-python
 nltk
 requests
 beautifulsoup4