Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+from transformers import pipeline
+import matplotlib.pyplot as plt
+from collections import Counter
+import threading
+
+# Initialize sentiment pipeline
+sentiment_analyzer = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english"
+)
+
+# Thread-safe log storage
+log_lock = threading.Lock()
+sentiment_log = []
+
+def analyze_and_log(text):
+    result = sentiment_analyzer(text)[0]
+    label = result['label']
+    score = round(result['score'], 3)
+    entry = f"Input: {text} --> Sentiment: {label} (Confidence: {score})"
+    
+    with log_lock:
+        sentiment_log.append((text, label))
+    
+    return label, score, entry, update_chart()
+
+def update_chart():
+    with log_lock:
+        labels = [label for _, label in sentiment_log]
+    counts = Counter(labels)
+    
+    fig, ax = plt.subplots(figsize=(4, 3))
+    ax.bar(counts.keys(), counts.values(), color=['#4CAF50', '#F44336'])
+    ax.set_title("Sentiment Distribution")
+    ax.set_xlabel("Sentiment")
+    ax.set_ylabel("Count")
+    plt.tight_layout()
+    
+    return fig
+
+with gr.Blocks() as demo:
+    gr.Markdown("# DistilBERT Sentiment Analysis with Live Logs & Visualization")
+    gr.Markdown("Enter your salon feedback or product review below and get instant sentiment analysis, logging, and sentiment summary visualization.")
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            input_text = gr.Textbox(lines=3, placeholder="Type your text here...")
+            analyze_btn = gr.Button("Analyze Sentiment")
+            sentiment_label = gr.Textbox(label="Sentiment Label", interactive=False)
+            confidence_score = gr.Textbox(label="Confidence Score", interactive=False)
+            log_output = gr.Textbox(label="Analysis Log", interactive=False, lines=10)
+            
+        with gr.Column(scale=2):
+            sentiment_chart = gr.Plot(label="Sentiment Distribution Chart")
+    
+    analyze_btn.click(
+        analyze_and_log,
+        inputs=input_text,
+        outputs=[sentiment_label, confidence_score, log_output, sentiment_chart]
+    )
+    
+if __name__ == "__main__":
+    demo.launch()