Create app.py

app.py

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+import os
+import requests
+import gradio as gr
+
+# This is the API endpoint for a pre-trained sentiment analysis model.
+# This specific model is a DistilBERT model fine-tuned for sentiment analysis.
+# The Hugging Face Inference API provides a generous free tier.
+# You can find other models here: https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads
+API_URL = "https://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english"
+
+API_TOKEN = os.getenv("HUGGING_FACE_API_TOKEN")
+
+# The headers for the API request, including the authorization token.
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+def analyze_sentiment(text):
+    """
+    Analyzes the sentiment of a given text using the Hugging Face Inference API.
+
+    Args:
+        text (str): The input text to analyze.
+
+    Returns:
+        str: A formatted string with the sentiment and confidence score,
+             or an error message if the API call fails.
+    """
+    if not API_TOKEN:
+        return "ERROR: Hugging Face API token not found. Please set the HUGGING_FACE_API_TOKEN environment variable."
+
+    if not text:
+        return "Please enter some text to analyze."
+
+    payload = {"inputs": text}
+
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload)
+        response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)
+        result = response.json()
+
+        # The API response is a list of lists. We'll grab the first item.
+        # Example response: [[{'label': 'POSITIVE', 'score': 0.9998782}, {'label': 'NEGATIVE', 'score': 0.00012176}]]
+        sentiment_data = result[0]
+        
+        # Find the sentiment with the highest score
+        top_sentiment = max(sentiment_data, key=lambda x: x['score'])
+        label = top_sentiment['label']
+        score = top_sentiment['score'] * 100  # Convert to percentage
+
+        return f"Sentiment: {label.upper()}\nConfidence: {score:.2f}%"
+
+    except requests.exceptions.RequestException as e:
+        # Handle network or API errors
+        return f"ERROR: Failed to connect to the API. Check your token and network connection. Details: {e}"
+    except Exception as e:
+        # Handle other potential errors
+        return f"ERROR: An unexpected error occurred. Details: {e}"
+
+# --- Gradio User Interface with Custom Styling ---
+
+# Custom CSS for a cute and aesthetic theme
+css = """
+body {
+    background: linear-gradient(135deg, #f7d9e2, #c7e0ff); /* Soft gradient background */
+    font-family: 'Comic Sans MS', 'Arial', sans-serif;
+}
+.gradio-container {
+    background-color: rgba(255, 255, 255, 0.7); /* Semi-transparent white background */
+    border-radius: 20px;
+    box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1); /* Soft shadow */
+    padding: 20px;
+}
+.gr-button {
+    background-color: #ff99cc; /* Pink button */
+    border: none;
+    color: white;
+    font-size: 1.1em;
+    border-radius: 15px;
+    transition: transform 0.2s ease-in-out;
+}
+.gr-button:hover {
+    background-color: #ff66b2; /* Darker pink on hover */
+    transform: scale(1.05); /* Slight grow effect */
+}
+.label-text {
+    font-size: 1.2em;
+    font-weight: bold;
+    color: #333;
+}
+.gr-text-box textarea {
+    border-radius: 10px;
+    border: 1px solid #ccc;
+    background-color: #fefefe;
+    padding: 10px;
+}
+"""
+
+# Create the Gradio interface using gr.Blocks for a custom layout
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+    gr.Markdown("# 🌸 The Happy-Go-Lucky Sentiment Analyzer 🌸")
+    gr.Markdown("A cute little AI friend that tells you the mood of your text!")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Image(
+                value="https://placehold.co/200x200/ffb3e6/333333?text=Cute+AI", 
+                label="Your AI Friend",
+                show_label=True,
+                show_download_button=False
+            )
+        with gr.Column(scale=3):
+            input_textbox = gr.Textbox(
+                lines=5, 
+                label="Tell me something!",
+                placeholder="Type your thoughts here, and I'll analyze the sentiment...",
+                info="I can tell you if your text is positive or negative."
+            )
+            analyze_button = gr.Button("💖 Analyze! 💖")
+    
+    output_label = gr.Label(label="Result", info="The sentiment and confidence score.")
+
+    # Event listener for the button click
+    analyze_button.click(fn=analyze_sentiment, inputs=input_textbox, outputs=output_label)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.launch()