✨ Uploaded core app files for real-time sentiment analysis

Includes app.py and requirements.txt using Gradio & Transformers

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the sentiment classification model
+classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+# Define the function for Gradio interface
+def classify_sentiment(text):
+    if not text.strip():
+        return "⚠️ Please enter a sentence to analyze.", ""
+    result = classifier(text)[0]
+    label = result['label']
+    confidence = round(result['score'] * 100, 2)
+    return f"**Prediction:** {label}", f"**Confidence:** {confidence}%"
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔍 Real-Time Sentiment Classifier")
+    gr.Markdown("""
+    ### 💡 Model Instruction:
+    This model is trained for binary sentiment classification — **Positive** and **Negative** only.  
+    Neutral or mixed opinions may be interpreted as leaning toward one side.  
+    For best results, input clearly positive or negative sentences.
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(
+                label="Enter your sentence below 👇",
+                placeholder='Example: "I love the features of this app!" or "The update ruined the experience."',
+                lines=3
+            )
+            analyze_button = gr.Button("Analyze Sentiment")
+
+        with gr.Column():
+            prediction_output = gr.Markdown()
+            confidence_output = gr.Markdown()
+
+    analyze_button.click(fn=classify_sentiment, inputs=text_input, outputs=[prediction_output, confidence_output])
+
+# Launch the app
+demo.launch()

requirements.txt

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+transformers
+torch
+gradio