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Update app.py
Browse files
app.py
CHANGED
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@@ -28,7 +28,7 @@ def preprocess_text(text):
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classifier_model = "Ginidu2003/Distilbert-Base-News-classifier"
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# ====================== COLORED BAR CHART ======================
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def create_colored_bar_chart(category_counts):
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if category_counts is None or len(category_counts) == 0:
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fig, ax = plt.subplots()
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@@ -38,17 +38,19 @@ def create_colored_bar_chart(category_counts):
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categories = category_counts["Category"]
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counts = category_counts["Count"]
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fig, ax = plt.subplots(figsize=(
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bars = ax.bar(categories, counts, color=colors)
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for bar in bars:
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height = bar.get_height()
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ax.text(bar.get_x() + bar.get_width()/2, height + 0.
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str(int(height)), ha='center', va='bottom', fontsize=
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ax.set_title("Category Distribution Across 5 Classes", fontsize=16, fontweight='bold')
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ax.set_xlabel("Category", fontsize=12)
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ax.set_ylabel("Count", fontsize=12)
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plt.xticks(rotation=15)
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@@ -116,14 +118,25 @@ def answer_question(news_content, question):
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except Exception as e:
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return f"Error: {str(e)}"
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# ====================== BEAUTIFUL
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with gr.Blocks(
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title="English News Classifier",
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theme=gr.themes.
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css="""
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.gradio-container {
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"""
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) as demo:
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@@ -131,16 +144,14 @@ with gr.Blocks(
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gr.Markdown("### Intelligent News Analysis Tool | Daily Mirror Sri Lanka")
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with gr.Tabs():
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# ====================== CLASSIFICATION TAB ======================
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with gr.Tab("π News Classification"):
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gr.Markdown("### Upload CSV and get automatic category prediction")
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)
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classify_btn = gr.Button("π Classify News", variant="primary", size="large")
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@@ -156,20 +167,10 @@ with gr.Blocks(
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outputs=[output_text, output_file, bar_chart]
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)
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# ====================== Q&A TAB ======================
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with gr.Tab("β Question Answering"):
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gr.Markdown("### Ask any question about a news article")
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lines=10,
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label="π Paste News Content",
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placeholder="Paste the full news article here..."
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)
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question_input = gr.Textbox(
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label="β Your Question",
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placeholder="e.g., What is the main issue? Who is involved?"
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)
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qa_btn = gr.Button("π Get Answer", variant="primary", size="large")
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qa_output = gr.Textbox(label="π‘ Answer", lines=6)
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)
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gr.Markdown("---")
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gr.Markdown("**Built for Text Analytics Assignment
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demo.launch()
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classifier_model = "Ginidu2003/Distilbert-Base-News-classifier"
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# ====================== BEAUTIFUL COLORED BAR CHART ======================
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def create_colored_bar_chart(category_counts):
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if category_counts is None or len(category_counts) == 0:
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fig, ax = plt.subplots()
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categories = category_counts["Category"]
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counts = category_counts["Count"]
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# Nice modern color palette
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colors = ['#3498DB', '#E67E22', '#9B59B6', '#2ECC71', '#E74C3C']
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fig, ax = plt.subplots(figsize=(11, 6))
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bars = ax.bar(categories, counts, color=colors, edgecolor='white', linewidth=0.8)
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# Add value on top of bars
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for bar in bars:
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height = bar.get_height()
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ax.text(bar.get_x() + bar.get_width()/2, height + 0.8,
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str(int(height)), ha='center', va='bottom', fontsize=13, fontweight='bold')
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ax.set_title("Category Distribution Across 5 Classes", fontsize=16, fontweight='bold', pad=20)
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ax.set_xlabel("Category", fontsize=12)
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ax.set_ylabel("Count", fontsize=12)
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plt.xticks(rotation=15)
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except Exception as e:
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return f"Error: {str(e)}"
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# ====================== BEAUTIFUL UI ======================
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with gr.Blocks(
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title="English News Classifier",
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theme=gr.themes.Dark(),
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css="""
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.gradio-container {
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max-width: 1200px;
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margin: auto;
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background: linear-gradient(180deg, #1a1a2e, #16213e);
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}
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h1 {
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text-align: center;
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font-size: 2.8rem;
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margin-bottom: 10px;
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}
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.tab-label {
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font-size: 1.2rem;
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font-weight: 600;
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}
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"""
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) as demo:
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gr.Markdown("### Intelligent News Analysis Tool | Daily Mirror Sri Lanka")
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with gr.Tabs():
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with gr.Tab("π News Classification"):
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gr.Markdown("### Upload CSV and get automatic category prediction")
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file_input = gr.File(
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label="π€ Upload your CSV file",
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file_types=[".csv"],
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height=140
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)
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classify_btn = gr.Button("π Classify News", variant="primary", size="large")
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outputs=[output_text, output_file, bar_chart]
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)
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with gr.Tab("β Question Answering"):
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gr.Markdown("### Ask any question about a news article")
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news_input = gr.Textbox(lines=12, label="π Paste News Content", placeholder="Paste the full news article here...")
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question_input = gr.Textbox(label="β Your Question", placeholder="e.g. What is the main topic?")
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qa_btn = gr.Button("π Get Answer", variant="primary", size="large")
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qa_output = gr.Textbox(label="π‘ Answer", lines=6)
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)
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gr.Markdown("---")
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gr.Markdown("**Built for Text Analytics Assignment - Section 02**")
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demo.launch()
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