Create app.py

app.py

@@ -0,0 +1,121 @@
+import gradio as gr
+from transformers import pipeline
+
+# ✅ Load Hugging Face Sentiment Analysis Pipeline
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+
+# ✅ Function to predict sentiment
+def predict_sentiment(movie_name, review, history):
+    result = sentiment_pipeline(review)[0]
+    label = result["label"]
+    score = result["score"]
+
+    sentiment_text = "🌟 Positive Review" if label == "POSITIVE" else "💔 Negative Review"
+    emoji = "😊" if label == "POSITIVE" else "😞"
+    sentiment_result = f"{sentiment_text} {emoji}  |  Confidence: {score:.2f}"
+
+    # Append to history
+    history.append((movie_name, review, sentiment_result, label))
+
+    # Build animated colorful cards
+    history_display = ""
+    for m_name, review_text, result_text, sentiment_label in reversed(history):
+        bg_color = "#D1F2EB" if sentiment_label == "POSITIVE" else "#FADBD8"
+        history_display += f"""
+        <div class="fade-card" style="background-color: {bg_color}; padding: 12px; margin-bottom: 10px; 
+            border-radius: 8px; box-shadow: 0 0 5px rgba(0,0,0,0.1); color: #000;">
+            <strong style="color:#000;">Movie:</strong> <span style="color:#000;">{m_name}</span><br>
+            <strong style="color:#000;">Review:</strong><br><span style="color:#000;">{review_text}</span><br><br>
+            <strong style="color:#000;">Result:</strong><br><span style="color:#000;">{result_text}</span>
+        </div>
+        """
+
+    return sentiment_result, history_display, history
+
+
+# ✅ Clear History Function
+def clear_history():
+    return "", "", "", []
+
+
+# ✅ Gradio App with Animation CSS
+with gr.Blocks(css="""
+body {
+    background-color: #FFF8E1;
+}
+.fade-card {
+    animation: fadeIn 1s ease-in-out;
+}
+@keyframes fadeIn {
+    0% { opacity: 0; transform: translateY(20px); }
+    100% { opacity: 1; transform: translateY(0); }
+}
+""", theme=gr.themes.Default(primary_hue="purple")) as demo:
+    gr.HTML(
+        """
+        <div style='
+            background: linear-gradient(to right, #FFF1EB, #ACE0F9);
+            padding: 30px;
+            border-radius: 12px;
+            box-shadow: 0 0 10px rgba(0,0,0,0.1);
+        '>
+            <h1 style='text-align: center; color: #6C63FF;'>🎥 Movie Review Sentiment Analyzer</h1>
+            <p style='text-align: center; color: black;'>
+                Analyze your review below to see if it feels 💔 Negative or 🌟 Positive.<br>
+                Previous reviews appear as colorful cards below.
+            </p>
+        </div>
+        <hr>
+        """
+    )
+
+    review_history = gr.State([])
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            movie_name = gr.Textbox(
+                label="🎬 Enter Your Movie Name",
+                placeholder="Ex: Leo, Don, Vikram, etc..."
+            )
+            review_input = gr.Textbox(
+                label="🎬 Your Movie Review",
+                placeholder="Type your movie review here...",
+                lines=5
+            )
+            with gr.Row():
+                clear_button = gr.Button("❌ Clear Input")
+                submit_button = gr.Button("✅ Analyze Review")
+                clear_history_button = gr.Button("🗑️ Clear All History")
+
+        with gr.Column(scale=1):
+            sentiment_output = gr.Textbox(
+                label="🔍 Sentiment Result",
+                interactive=False
+            )
+
+    history_box = gr.HTML(label="📝 Review History")
+
+    submit_button.click(
+        fn=predict_sentiment,
+        inputs=[movie_name, review_input, review_history],
+        outputs=[sentiment_output, history_box, review_history]
+    )
+
+    clear_button.click(
+        lambda: ("", "",),
+        None,
+        [movie_name, review_input],
+        queue=False
+    )
+
+    clear_history_button.click(
+        fn=clear_history,
+        inputs=None,
+        outputs=[sentiment_output, history_box, review_history],
+        queue=False
+    )
+
+
+# ✅ Launch App
+demo.launch()