Delete app.py

app.py

@@ -1,93 +0,0 @@
-import gradio as gr
-#from transformers import pipeline
-
-# Load the sentiment analysis pipeline
-# We use a model specifically trained on product reviews (Amazon reviews)
-model_name = "LiYuan/amazon-review-sentiment-analysis"
-sentiment_pipeline = pipeline("sentiment-analysis", model=model_name)
-
-def analyze_sentiment(review_text):
-    """
-    Analyzes the sentiment of the input text and returns a formatted result.
-    The model outputs star ratings (1-5 stars).
-    """
-    if not review_text.strip():
-        return "Please enter some text to analyze.", None
-
-    try:
-        # Perform sentiment analysis
-        results = sentiment_pipeline(review_text)
-        
-        # The model returns labels like '1 star', '2 stars', etc.
-        label = results[0]['label']
-        score = results[0]['score']
-        
-        # Map star ratings to sentiment categories
-        star_count = int(label.split()[0])
-        
-        if star_count >= 4:
-            sentiment = "Positive"
-            color = "🟢"
-        elif star_count == 3:
-            sentiment = "Neutral"
-            color = "🟡"
-        else:
-            sentiment = "Negative"
-            color = "🔴"
-            
-        result_text = f"### Sentiment: {sentiment} {color}\n"
-        result_text += f"**Rating:** {label} ({score:.2%} confidence)\n\n"
-        
-        # Add some context for computer system products
-        if "battery" in review_text.lower():
-            result_text += "- *Note: This review mentions battery life.*\n"
-        if "performance" in review_text.lower() or "fast" in review_text.lower() or "slow" in review_text.lower():
-            result_text += "- *Note: This review mentions system performance.*\n"
-        if "screen" in review_text.lower() or "display" in review_text.lower():
-            result_text += "- *Note: This review mentions the display/screen.*\n"
-            
-        return result_text, {label: score}
-        
-    except Exception as e:
-        return f"Error during analysis: {str(e)}", None
-
-# Define the Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 💻 Computer System Sentiment Analyzer")
-    gr.Markdown(
-        "Enter a review for a computer, laptop, or hardware component to analyze its sentiment. "
-        "This tool uses a model trained on millions of product reviews to provide accurate star ratings."
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            input_text = gr.Textbox(
-                label="Product Review",
-                placeholder="e.g., The MacBook Pro has amazing performance and a stunning display, but the price is a bit high...",
-                lines=5
-            )
-            submit_btn = gr.Button("Analyze Sentiment", variant="primary")
-            
-        with gr.Column():
-            output_markdown = gr.Markdown(label="Analysis Result")
-            output_label = gr.Label(label="Confidence Score")
-
-    # Examples for users to try
-    gr.Examples(
-        examples=[
-            ["The laptop is incredibly fast and the battery lasts all day. Highly recommended!"],
-            ["The screen arrived with dead pixels and the customer service was unhelpful. Disappointed."],
-            ["It's a decent computer for the price. Not the fastest, but gets the job done for basic tasks."],
-            ["The cooling system is quite loud under load, but the gaming performance is top-notch."]
-        ],
-        inputs=input_text
-    )
-
-    submit_btn.click(
-        fn=analyze_sentiment,
-        inputs=input_text,
-        outputs=[output_markdown, output_label]
-    )
-
-if __name__ == "__main__":
-    demo.launch()