Update app.py

app.py

@@ -1,63 +1,58 @@
 import gradio as gr
-from transformers import pipeline
-
-# Load Hugging Face models
-# Text generation for keyword suggestions
-keyword_generator = pipeline("text-generation", model="gpt2", tokenizer="gpt2")
-
-# Sentiment analysis for niche review insights
-sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-
-
-# Function to generate keyword suggestions
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# Initialize the keyword generator pipeline with error handling
+try:
+    # Load model and tokenizer
+    model_name = "gpt2"
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    keyword_generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    print("Model loaded successfully!")
+except Exception as e:
+    keyword_generator = None
+    print(f"Error loading model: {e}")
+
+# Function to generate keywords
 def suggest_keywords(prompt):
-    results = keyword_generator(prompt, max_length=50, num_return_sequences=3)
-    suggestions = [res["text"].strip() for res in results]
-    return "\n".join(suggestions)
-
-
-# Function to analyze sentiment of user-input text
+    if not keyword_generator:
+        return "Model failed to load. Please check the logs or environment."
+    
+    try:
+        results = keyword_generator(prompt, max_length=50, num_return_sequences=3)
+        suggestions = [res["text"].strip() for res in results]
+        return "\n".join(suggestions)
+    except Exception as e:
+        return f"Error generating keywords: {e}"
+
+# Function for sentiment analysis
 def analyze_sentiment(text):
-    sentiments = sentiment_analyzer(text)
-    return sentiments
+    try:
+        sentiment_pipeline = pipeline("sentiment-analysis")
+        result = sentiment_pipeline(text)[0]
+        return f"Label: {result['label']}, Confidence: {result['score']:.2f}"
+    except Exception as e:
+        return f"Error performing sentiment analysis: {e}"
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# KDP Keyword Suggestion App")
+    gr.Markdown("Generate profitable KDP coloring book niches and analyze customer feedback!")
 
-
-# Gradio Interface Design
-with gr.Blocks() as app:
-    gr.Markdown(
-        """
-        # KDP Keyword Suggestion App
-        Generate profitable KDP coloring book niches and analyze customer feedback!
-        """
-    )
-
-    # Section for keyword generation
     with gr.Row():
         with gr.Column():
-            prompt_input = gr.Textbox(
-                label="Enter Keyword Prompt",
-                placeholder="E.g., coloring book for kids about",
-            )
-            keyword_output = gr.Textbox(label="Generated Keywords", lines=5)
-
-        keyword_button = gr.Button("Generate Keywords")
-        keyword_button.click(suggest_keywords, inputs=prompt_input, outputs=keyword_output)
-
-    # Section for sentiment analysis
-    with gr.Row():
+            gr.Markdown("### Enter Keyword Prompt")
+            keyword_input = gr.Textbox(label="Enter Keyword Prompt")
+            keyword_output = gr.Textbox(label="Generated Keywords")
+            generate_button = gr.Button("Generate Keywords")
+        
         with gr.Column():
-            review_input = gr.Textbox(
-                label="Enter Text for Sentiment Analysis",
-                placeholder="Paste a customer review or feedback here...",
-                lines=4,
-            )
-            sentiment_output = gr.Label(label="Sentiment Analysis Result")
-
-        sentiment_button = gr.Button("Analyze Sentiment")
-        sentiment_button.click(analyze_sentiment, inputs=review_input, outputs=sentiment_output)
+            gr.Markdown("### Enter Text for Sentiment Analysis")
+            sentiment_input = gr.Textbox(label="Paste a customer review or feedback here")
+            sentiment_output = gr.Textbox(label="Sentiment Analysis Result")
+            sentiment_button = gr.Button("Analyze Sentiment")
 
-    # Footer
-    gr.Markdown("Built with ❤️ using Hugging Face and Gradio for KDP enthusiasts!")
+    generate_button.click(suggest_keywords, inputs=keyword_input, outputs=keyword_output)
+    sentiment_button.click(analyze_sentiment, inputs=sentiment_input, outputs=sentiment_output)
 
-# Launch the app
-app.launch()
+demo.launch()