moved to a simpler generator, changed UI by adding a prefill button

app.py

@@ -6,154 +6,25 @@ from datetime import datetime, timedelta
 import requests
 from bs4 import BeautifulSoup
 
-# Download required NLTK data
-try:
-    nltk.data.find('tokenizers/punkt')
-except LookupError:
-    nltk.download('punkt')
+# Previous imports and model loading code remains the same...
+# (Keep all the previous code until the create_interface function)
 
-# Initialize models and tokenizers
-def load_models():
-    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):
-    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, timeout=5)
-        soup = BeautifulSoup(response.content, 'xml')
-        items = soup.find_all('item', limit=num_articles)
-        
-        news_data = []
-        for item in items:
-            try:
-                news_data.append({
-                    'title': item.title.text,
-                    'description': item.description.text if item.description else ""
-                })
-            except:
-                continue
-        
-        return news_data
-    except Exception as e:
-        return [{'title': f'Using default context due to error: {str(e)}', 'description': ''}]
-
-# 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,
-    content_checker
-):
-    # 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 context: {item['title']}" for item in news_data])
-    
-    # Create prompt
-    prompt = f"""
-    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}
-    
-    Generate a compelling {platform} post that highlights the product's benefits while maintaining a {tone} tone.
-    """
-    
-    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,
-        )
-        
-        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 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}"
-                })
-        
-        return filtered_content
-    except Exception as e:
-        print(f"Error generating content: {str(e)}")
-        return []
-
-# Gradio interface
 def create_interface():
+    print("Loading models...")
     generator_tokenizer, generator, sentiment_analyzer, content_checker = load_models()
+    print("Models loaded successfully!")
+    
+    # Sample data function
+    def fill_sample_data():
+        return [
+            "EcoBottle",  # Product Name
+            "Sustainable water bottle made from recycled ocean plastic",  # Product Description
+            "Environmentally conscious young professionals",  # Target Audience
+            "100% recycled materials, Insulated design, Leak-proof",  # Key Features
+            "Helps clean oceans, Keeps drinks cold for 24 hours",  # Unique Benefits
+            "Twitter",  # Platform
+            "professional"  # Tone
+        ]
     
     def process_input(
         product_name,
@@ -179,40 +50,51 @@ def create_interface():
                 content_checker
             )
             
-            if not results:
-                return "No suitable content generated. Please try again with different parameters."
-            
-            output = ""
+            output = "🎯 Generated Marketing Content:\n\n"
             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 += f"Version {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 input components
+    product_name = gr.Textbox(label="Product Name", placeholder="Enter product name")
+    product_description = gr.Textbox(label="Product Description", lines=3, placeholder="Brief description of your product")
+    target_audience = gr.Textbox(label="Target Audience", placeholder="Who is this product for?")
+    key_features = gr.Textbox(label="Key Features", lines=2, placeholder="Main features of your product")
+    unique_benefits = gr.Textbox(label="Unique Benefits", lines=2, placeholder="What makes your product special?")
+    platform = gr.Radio(
+        choices=["Twitter", "Instagram"],
+        label="Platform",
+        value="Twitter"
+    )
+    tone = gr.Textbox(label="Tone", placeholder="e.g., professional, casual, friendly")
     
-    # Create the interface
+    # Output component
+    output = gr.Textbox(label="Generated Content", lines=10)
+
+    # Create the interface with custom layout
     iface = gr.Interface(
         fn=process_input,
         inputs=[
-            gr.Textbox(label="Product Name", placeholder="Enter product name"),
-            gr.Textbox(label="Product Description", lines=3, placeholder="Brief description of your product"),
-            gr.Textbox(label="Target Audience", placeholder="Who is this product for?"),
-            gr.Textbox(label="Key Features", lines=2, placeholder="Main features of your product"),
-            gr.Textbox(label="Unique Benefits", lines=2, placeholder="What makes your product special?"),
-            gr.Radio(
-                choices=["Twitter", "Instagram"],
-                label="Platform",
-                value="Twitter"
-            ),
-            gr.Textbox(label="Tone", placeholder="e.g., professional, casual, friendly"),
+            product_name,
+            product_description,
+            target_audience,
+            key_features,
+            unique_benefits,
+            platform,
+            tone
         ],
-        outputs=gr.Textbox(label="Generated Content", lines=10),
+        outputs=output,
         title="Ethimar - AI Marketing Content Generator",
-        description="Generate ethical marketing content with AI-powered insights",
+        description="""Generate ethical marketing content with AI-powered insights.
+        ⏳ Note: First generation might take 3-5 minutes due to model loading.
+        Subsequent generations will be faster!""",
         theme="default",
         examples=[
             [
@@ -226,7 +108,29 @@ def create_interface():
             ]
         ]
     )
+
+    # Add the sample data button with custom styling
+    fill_button = gr.Button(
+        "Fill the form with sample data",
+        variant="primary",
+        scale=1,
+        size="sm"
+    )
     
+    # Connect the button to the fill_sample_data function
+    fill_button.click(
+        fn=fill_sample_data,
+        outputs=[
+            product_name,
+            product_description,
+            target_audience,
+            key_features,
+            unique_benefits,
+            platform,
+            tone
+        ]
+    )
+
     return iface
 
 # Launch the app