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app.py

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+import gradio as gr
+import nltk
+import joblib
+
+nltk.download("punkt")
+nltk.download("wordnet")
+nltk.download('punkt_tab')
+
+model = joblib.load("sentiment_pipeline.joblib")
+
+def predict_sentiment(text, show_preprocessed=False):
+    proba = model.predict_proba([text])[0]
+    sentiment = "Positive 😀" if proba[1] >= 0.5 else "Negative 😞"
+    confidence = f"{round(max(proba) * 100, 2)}%"
+    pre_out = ""
+    if show_preprocessed:
+        pre_out = model.named_steps["textpreprocessor"].transform([text])[0]
+    return sentiment, confidence, pre_out
+
+with gr.Blocks(css="""
+.pipeline-container {
+    background-color: blue;
+    border:1px solid #ddd;
+    border-radius:8px;
+    padding:8px;
+    margin-bottom: 4px;
+}
+.footer {
+    margin-top: 24px;
+    font-size:0.9rem;
+    text-align:center;
+}
+""") as demo:
+
+    gr.Markdown("# 🎬 SentiMDB")
+    gr.Markdown(
+        "### SentiMDB is a lightweight, production-ready Sentiment Analysis Pipeline based on IMDb movie reviews. It features a Flask web app, a Dockerized setup for easy deployment, and a Hugging Face Spaces-powered online demo. The project includes a comprehensive Jupyter Notebook, offering a guide to English Text Preprocessing and detailing the full Machine Learning Development process, including Model Selection, Error Analysis, and Fine-Tuning. By leveraging classic machine learning tools alone, the model achieved 91.67% prediction accuracy."
+    )
+  
+
+    with gr.Row():
+        with gr.Column(scale=1):
+
+          
+            gr.HTML("""
+            <div class="pipeline-container">
+              <h4 style="text-align:center; margin:0 0 8px 0;">Pipeline</h4>
+              <div style="display:flex; justify-content:space-around; align-items:center;">
+                <div>📝 Input Text</div>
+                <div>→</div>
+                <div>🔧 TextPreprocessor</div>
+                <div>→</div>
+                <div>📊 TF‑IDF</div>
+                <div>→</div>
+                <div>🤖 Logistic Regressor</div>
+              </div>
+            </div>
+            """)
+
+            
+            
+            review = gr.Textbox(lines=3, placeholder="Type your movie review here…")
+            show_pre = gr.Checkbox(label="Show Preprocessed text", value=True)
+            analyze_btn = gr.Button("Analyze", variant="primary")
+
+            
+
+        with gr.Column(scale=1):
+           
+            sentiment_out = gr.Label(label="Sentiment")
+            confidence_out = gr.Textbox(label="Confidence")
+            pre_out = gr.Textbox(label="Preprocessed Text", interactive=False)
+
+            
+
+    
+    analyze_btn.click(
+        fn=predict_sentiment,
+        inputs=[review, show_pre],
+        outputs=[sentiment_out, confidence_out, pre_out]
+    )
+
+    
+    gr.HTML(
+        '<div class="footer">For the full project Jupyter Notebook, Flask Web App & Docker Config, visit: <a href="https://github.com/Hoom4n/SentiMDB" target="_blank">https://github.com/Hoom4n/SentiMDB</a></div>'
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+scikit-learn==1.6.1
+joblib==1.5.0
+hoomanmltk==0.1.0
+nltk==3.9.1
+gradio

sentiment_pipeline.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ca2d97da646a42395dc8f2bb887cea2cdbd5ae39b8f52914f5c15f86696e967
+size 1823252