new files

app.py

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+# pylint: disable=import-error
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from transformers import pipeline
+
+# Load pre-trained sentiment analysis model
+sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+def analyze_sentiment(text):
+    """
+    Analyze the sentiment of input text
+    """
+    if not text or not text.strip():
+        return {
+            "Sentiment": "N/A",
+            "Confidence": 0,
+            "Positive": 0,
+            "Negative": 0
+        }
+        
+    result = sentiment_analyzer(text)[0]
+    sentiment = result["label"]
+    confidence = result["score"]
+    
+    # Create result dictionary
+    output = {
+        "Sentiment": "Positive" if sentiment == "POSITIVE" else "Negative",
+        "Confidence": round(confidence * 100, 2)
+    }
+    
+    # Add values for the gauge chart
+    output["Positive"] = confidence if sentiment == "POSITIVE" else 1 - confidence
+    output["Negative"] = 1 - output["Positive"]
+    
+    return output
+
+def process_text(text):
+    """
+    Process the text and create visualizations
+    """
+    result = analyze_sentiment(text)
+    
+    # Create a visual representation of the sentiment
+    labels = ['Positive', 'Negative']
+    sizes = [result["Positive"], result["Negative"]]
+    colors = ['#4CAF50', '#F44336']
+    
+    fig, ax = plt.subplots(figsize=(5, 3))
+    ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
+    ax.axis('equal')
+    plt.title('Sentiment Analysis')
+    
+    return result["Sentiment"], result["Confidence"], fig
+
+# Define examples for users to try
+examples = [
+    ["I love this product! It's absolutely fantastic and exceeded all my expectations."],
+    ["This was a terrible experience. I will never use this service again."],
+    ["The quality is okay, but the price is a bit high for what you get."],
+    ["I've had better, but I've also had much worse. It's a decent option."],
+    ["This is the best decision I've ever made. Highly recommended!"]
+]
+
+# Create Gradio interface
+with gr.Blocks(title="Sentiment Analyzer", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 💬 Text Sentiment Analyzer
+        
+        This interactive tool analyzes the sentiment of any text, determining whether it's positive or negative. 
+        It's particularly useful for analyzing customer feedback, social media comments, or product reviews.
+        
+        Try typing or pasting text in the input area below, or select one of the examples to see how it works!
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            text_input = gr.Textbox(
+                label="Enter text to analyze",
+                placeholder="Type or paste text here...",
+                lines=5
+            )
+            
+            analyze_btn = gr.Button("Analyze Sentiment", variant="primary")
+        
+        with gr.Column(scale=2):
+            sentiment_label = gr.Label(label="Result")
+            confidence = gr.Number(label="Confidence Score (%)")
+            sentiment_gauge = gr.Plot(label="Sentiment Distribution")
+    
+    # Add examples section
+    gr.Examples(
+        examples=examples,
+        inputs=text_input,
+        outputs=[sentiment_label, confidence, sentiment_gauge],
+        fn=process_text,
+        cache_examples=True
+    )
+    
+    # Set up the click event
+    analyze_btn.click(
+        fn=process_text,
+        inputs=text_input,
+        outputs=[sentiment_label, confidence, sentiment_gauge]
+    )
+    
+    gr.Markdown("""
+    ### How it works
+    
+    This tool uses a DistilBERT model fine-tuned for sentiment analysis. The model has been trained on a large dataset 
+    of text with positive and negative sentiments, allowing it to recognize emotional tone in written text.
+    
+    ### Applications
+    
+    - **Customer Service**: Monitor customer feedback in real-time
+    - **Market Research**: Analyze opinions about products or services
+    - **Social Media Monitoring**: Track sentiment about your brand across platforms
+    - **Content Analysis**: Evaluate the emotional impact of your content
+    
+    Created by [Vinicius Guerra e Ribas](https://viniciusgribas.netlify.app/)
+    """)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==5.21.0
+gradio_client==1.7.2
+matplotlib==3.10.1
+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1733416936468/work
+numpy==2.1.2
+torch==2.6.0+cpu
+torchvision==0.21.0+cpu
+transformers==4.49.0