Requirements.txt

Appy.py

@@ -0,0 +1,139 @@
+from textblob import TextBlob
+import gradio as gr
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+def final_sentiment_app(text):
+    if not text.strip():
+        return "⚠️ Please enter some text!", None
+
+    sentences = [s.strip() for s in text.replace('!','!|').replace('?','?|').replace('.','.|').split('|') if s.strip()]
+
+    if len(sentences) == 0:
+        return "⚠️ No sentences found!", None
+
+    scores = []
+    colors = []
+    sentence_results = []
+    pos_count = neg_count = neu_count = 0
+    total_score = 0
+
+    for i, sentence in enumerate(sentences):
+        blob = TextBlob(sentence)
+        score = round(blob.sentiment.polarity, 2)
+        subjectivity = round(blob.sentiment.subjectivity, 2)
+        scores.append(score)
+        total_score += score
+
+        if score >= 0.5:
+            label = "🤩 Very Positive"
+            colors.append('#00e676')
+            pos_count += 1
+        elif score > 0.1:
+            label = "😊 Positive"
+            colors.append('#00c853')
+            pos_count += 1
+        elif score <= -0.5:
+            label = "😡 Very Negative"
+            colors.append('#ff1744')
+            neg_count += 1
+        elif score < -0.1:
+            label = "😞 Negative"
+            colors.append('#d50000')
+            neg_count += 1
+        else:
+            label = "😐 Neutral"
+            colors.append('#aa00ff')
+            neu_count += 1
+
+        sentence_results.append(
+            f"S{i+1} {label} | Score: {score} | Subjectivity: {subjectivity}\n"
+            f"    → \"{sentence}\""
+        )
+
+    avg = round(total_score / len(sentences), 2)
+
+    if avg >= 0.5:    overall = "🤩 Very Positive"
+    elif avg > 0.1:   overall = "😊 Positive"
+    elif avg <= -0.5: overall = "😡 Very Negative"
+    elif avg < -0.1:  overall = "😞 Negative"
+    else:             overall = "😐 Neutral"
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))
+    fig.patch.set_facecolor('#0d0d1a')
+    fig.suptitle('Sentiment Analysis Report', color='white', fontsize=14, fontweight='bold')
+
+    ax1.set_facecolor('#1a1a2e')
+    x = range(len(sentences))
+    bars = ax1.bar(x, scores, color=colors, edgecolor='white', linewidth=0.5, width=0.5)
+    ax1.axhline(y=0, color='white', linewidth=1, linestyle='--', alpha=0.5)
+    ax1.set_title('Score Per Sentence', color='white', fontsize=11)
+    ax1.set_xticks(x)
+    ax1.set_xticklabels([f'S{i+1}' for i in x], color='white')
+    ax1.set_ylabel('Polarity (-1 to +1)', color='white')
+    ax1.set_ylim(-1.2, 1.2)
+    ax1.tick_params(colors='white')
+    for bar, score in zip(bars, scores):
+        ypos = bar.get_height()+0.05 if score >= 0 else bar.get_height()-0.12
+        ax1.text(bar.get_x()+bar.get_width()/2, ypos,
+                str(score), ha='center', color='white', fontsize=9, fontweight='bold')
+
+    ax2.set_facecolor('#1a1a2e')
+    pie_data = [(pos_count,'Positive\n','#00c853'),
+                (neg_count,'Negative\n','#ff1744'),
+                (neu_count,'Neutral\n','#aa00ff')]
+    pie_data = [(v,l,c) for v,l,c in pie_data if v > 0]
+    ax2.pie(
+        [d[0] for d in pie_data],
+        labels=[f"{d[1]}({d[0]})" for d in pie_data],
+        colors=[d[2] for d in pie_data],
+        autopct='%1.0f%%',
+        textprops={'color':'white','fontsize':10},
+        wedgeprops={'edgecolor':'white','linewidth':1.2},
+        startangle=90
+    )
+    ax2.set_title('Sentiment Distribution', color='white', fontsize=11)
+    plt.tight_layout()
+
+    report = "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
+    report += "📋 SENTENCE BREAKDOWN\n"
+    report += "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
+    report += "\n".join(sentence_results)
+    report += "\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
+    report += "📊 FINAL SUMMARY\n"
+    report += "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
+    report += f"🎯 Overall Sentiment  : {overall}\n"
+    report += f"📈 Average Score      : {avg}\n"
+    report += f"✅ Positive sentences : {pos_count}\n"
+    report += f"❌ Negative sentences : {neg_count}\n"
+    report += f"➖ Neutral sentences  : {neu_count}\n"
+    report += f"📝 Total sentences    : {len(sentences)}\n"
+    report += f"📏 Total words        : {len(text.split())}\n"
+    report += "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+
+    return report, fig
+
+app = gr.Interface(
+    fn=final_sentiment_app,
+    inputs=gr.Textbox(
+        placeholder="Paste a paragraph, product review, tweet or any text...",
+        label="📝 Enter Your Text",
+        lines=6
+    ),
+    outputs=[
+        gr.Textbox(label="📋 Analysis Report", lines=18),
+        gr.Plot(label="📈 Visual Charts")
+    ],
+    title="🧠 Sentiment Analyzer Pro",
+    description="✨ AI-powered sentiment detection | Sentence-by-sentence breakdown | Visual charts | Built with Python & NLP",
+    examples=[
+        ["I love this college! The canteen food is terrible. Classes are okay. Teachers are amazing!"],
+        ["This phone is fantastic! Battery life is poor. Camera quality is outstanding. Delivery was late."],
+        ["I am so happy today! Work was stressful. But my friends made it better. Overall a good day!"],
+        ["The movie was boring. Acting was terrible. But the music was absolutely amazing!"]
+    ],
+    theme="soft"
+)
+
+app.launch()