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Initial code update

README.md

@@ -1,14 +1,19 @@
----
-title: Sentiment Analysis On Traveler Reviews
-emoji: 📈
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 5.25.2
-app_file: app.py
-pinned: false
-license: mit
-short_description: This is a sentiment analysis model.
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+ # Traveler Review Sentiment Analysis
+
+This is a sentiment analysis model that predicts whether a traveller's review is positive or negative. The model uses TF-IDF vectorization and a trained classifier to make predictions.
+
+## How to Use
+
+1. Enter your review in the text box
+2. Click "Analyze Sentiment"
+3. The model will predict whether the review is positive or negative
+
+## Model Details
+
+- Uses TF-IDF vectorization for text preprocessing
+- Trained on movie review data
+- Returns either "Positive Review 😊" or "Negative Review 😞"
+
+## Requirements
+
+All required packages are listed in `requirements.txt`

gradio_app.py

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+import gradio as gr
+import pickle as pk
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+import os
+
+# Get the absolute path of the current directory
+current_dir = os.path.dirname(os.path.abspath(__file__))
+model_path = os.path.join(current_dir, 'model.pkl')
+scaler_path = os.path.join(current_dir, 'scaler.pkl')
+
+def load_model_and_vectorizer():
+    try:
+        if not os.path.exists(model_path) or not os.path.exists(scaler_path):
+            return None, None, f"Error: Model files not found at:\n{model_path}\n{scaler_path}"
+        
+        model = pk.load(open(model_path, 'rb'))
+        vectorizer = pk.load(open(scaler_path, 'rb'))
+        return model, vectorizer, None
+    except Exception as e:
+        return None, None, f"Error loading model: {str(e)}"
+
+# Load the model and vectorizer
+model, vectorizer, error = load_model_and_vectorizer()
+
+def predict_sentiment(review):
+    if error:
+        return error
+    
+    if not review:
+        return "Please enter a review first!"
+    
+    try:
+        # Vectorize the text
+        review_vector = vectorizer.transform([review])
+        # Make prediction
+        result = model.predict(review_vector)[0]
+        
+        if result == 0:
+            return "Negative Review 😞"
+        else:
+            return "Positive Review 😊"
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
+
+# Modern and aesthetic CSS adapted from the dashboard
+css = """
+body {
+    background: linear-gradient(180deg, #2c3e50 0%, #3498db 100%);
+    margin: 0;
+    font-family: 'Roboto', -apple-system, BlinkMacSystemFont, sans-serif;
+    color: #ecf0f1;
+}
+
+.gradio-container {
+    max-width: 800px;
+    margin: 3rem auto;
+    background: rgba(44, 62, 80, 0.9);
+    border-radius: 20px;
+    padding: 2.5rem;
+    box-shadow: 0 12px 40px rgba(0, 0, 0, 0.3);
+    backdrop-filter: blur(10px);
+    transition: transform 0.3s ease;
+}
+
+.gradio-container:hover {
+    transform: translateY(-5px);
+}
+
+.header {
+    text-align: center;
+    margin-bottom: 2rem;
+    animation: fadeIn 1s ease-in-out;
+}
+
+.header h3 {
+    font-size: 2.4rem;
+    background: linear-gradient(45deg, #e74c3c, #f1c40f);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    margin: 0;
+    font-weight: 700;
+}
+
+.header p {
+    font-size: 1.2rem;
+    color: #bdc3c7;
+    margin-top: 0.5rem;
+    font-weight: 300;
+}
+
+.review-input {
+    background: rgba(255, 255, 255, 0.1);
+    border: 2px solid #4ECDC4;
+    border-radius: 12px;
+    padding: 1.5rem;
+    font-size: 1.1rem;
+    color: #ecf0f1;
+    width: 100%;
+    box-sizing: border-box;
+    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
+    transition: all 0.3s ease;
+}
+
+.review-input:focus {
+    border-color: #f1c40f;
+    box-shadow: 0 6px 20px rgba(241, 196, 15, 0.3);
+    outline: none;
+    transform: scale(1.01);
+}
+
+.review-input::placeholder {
+    color: #95a5a6;
+    font-style: italic;
+}
+
+.submit-btn {
+    background: linear-gradient(45deg, #FF6B6B, #4ECDC4);
+    color: white;
+    border: none;
+    padding: 1rem 3rem;
+    border-radius: 50px;
+    font-size: 1.1rem;
+    font-weight: 600;
+    cursor: pointer;
+    margin: 1.5rem auto;
+    display: block;
+    transition: all 0.3s ease;
+    box-shadow: 0 6px 20px rgba(78, 205, 196, 0.4);
+}
+
+.submit-btn:hover {
+    background: linear-gradient(45deg, #4ECDC4, #FF6B6B);
+    transform: translateY(-3px) scale(1.05);
+    box-shadow: 0 10px 25px rgba(78, 205, 196, 0.5);
+}
+
+.submit-btn:active {
+    transform: translateY(0);
+    box-shadow: 0 4px 15px rgba(78, 205, 196, 0.3);
+}
+
+.output-container {
+    margin-top: 2rem;
+    padding: 1.5rem;
+    background: rgba(255, 255, 255, 0.05);
+    border-radius: 15px;
+    text-align: center;
+    min-height: 80px;
+    font-size: 1.3rem;
+    color: #f1c40f;
+    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
+    animation: slideUp 0.5s ease-out;
+}
+
+.error-message {
+    background: rgba(231, 76, 60, 0.2);
+    color: #e74c3c;
+    padding: 1.2rem;
+    border-radius: 12px;
+    margin-bottom: 1.5rem;
+    border-left: 5px solid #e74c3c;
+    font-weight: 500;
+    animation: shake 0.4s ease-in-out;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(-10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+@keyframes slideUp {
+    from { opacity: 0; transform: translateY(20px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+@keyframes shake {
+    0%, 100% { transform: translateX(0); }
+    20%, 60% { transform: translateX(-8px); }
+    40%, 80% { transform: translateX(8px); }
+}
+
+@media (max-width: 600px) {
+    .gradio-container {
+        margin: 1rem;
+        padding: 1.5rem;
+    }
+    
+    .header h1 {
+        font-size: 2rem;
+    }
+    
+    .review-input {
+        padding: 1rem;
+    }
+    
+    .submit-btn {
+        padding: 0.8rem 2rem;
+    }
+}
+"""
+
+# Create the Gradio interface
+with gr.Blocks(
+    title="Sentiment Analysis on Traveler Reviews",
+    theme=gr.themes.Default(
+        primary_hue="teal",
+        secondary_hue="pink",
+        neutral_hue="gray",
+        radius_size="lg",
+    ),
+    css=css
+) as demo:
+    gr.Markdown("""
+    <div class="header">
+        <h3> Sentiment Analysis on Traveler Reviews</h3>
+        <p>Type your review and let our AI uncover its vibe!</p>
+    </div>
+    """)
+    
+    if error:
+        gr.Markdown(f"""
+        <div class="error-message">
+            ⚠️ {error}
+        </div>
+        """)
+    
+    review_input = gr.Textbox(
+        placeholder="Share your thoughts about the your experience...",
+        lines=5,
+        elem_classes="review-input",
+        show_label=False
+    )
+    
+    predict_btn = gr.Button(
+        "Analyze Sentiment",
+        elem_classes="submit-btn"
+    )
+    
+    output = gr.Textbox(
+        placeholder="The sentiment will appear here...",
+        elem_classes="output-container",
+        show_label=False,
+        interactive=False
+    )
+    
+    predict_btn.click(
+        fn=predict_sentiment,
+        inputs=review_input,
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d095b620dc4c01338432db9a4e67040ccbff98ea59abf59a0d13800e3f4ebf0
+size 20713

requirements.txt

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+ gradio>=3.50.0
+scikit-learn>=1.0.0
+pandas>=1.3.0
+numpy>=1.21.0

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:56d0af5dc67f06c0602b6b70687d7d1293181f20ab78b7c5da25f7dcab1f6c0e
+size 265654