Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import cluster_news
+import extract_news
+import summarizer
+import analyze_sentiment
+import gather_news
+
+# ------------------ Utilities ------------------
+
+def fetch_content(topic):
+    articles = gather_news.fetch_articles_newsapi(topic)
+    if isinstance(articles, str):
+        articles = gather_news.fetch_articles_google(topic)
+        if isinstance(articles, str):
+            return None
+    try:
+        articles = sorted(articles, key=lambda x: x.get("publishedAt", ""), reverse=True)[:10]
+    except Exception:
+        return None
+    return articles
+
+def fetch_and_process_latest_news(sentiment_filters):
+    topic = "Top Headlines"
+    articles = gather_news.fetch_articles_newsapi("top headlines")
+    if isinstance(articles, str) or not articles:
+        return sentiment_filters, "### No latest news available", "", "", "", "", None
+
+    articles = sorted(articles, key=lambda x: x.get("publishedAt", ""), reverse=True)[:10]
+    extracted_articles = extract_summarize_and_analyze_articles(articles)
+
+    if not extracted_articles:
+        return sentiment_filters, "### No content to display", "", "", "", "", None
+
+    df = pd.DataFrame(extracted_articles)
+    result = cluster_news.cluster_and_label_articles(df, content_column="content", summary_column="summary")
+    cluster_md_blocks = display_clusters_as_columns(result, sentiment_filters)
+    csv_file, _ = save_clustered_articles(result["dataframe"], topic)
+
+    return sentiment_filters, *cluster_md_blocks, csv_file
+
+def extract_summarize_and_analyze_articles(articles):
+    extracted_articles = []
+    for article in articles:
+        url = article.get("url")
+        if url:
+            content, _ = extract_news.extract_full_content(url)
+            if content:
+                summary = summarizer.generate_summary(content)
+                sentiment, score = analyze_sentiment.analyze_summary(summary)
+                extracted_articles.append({
+                    "title": article.get("title", "No title"),
+                    "url": url,
+                    "source": article.get("source", "Unknown"),
+                    "author": article.get("author", "Unknown"),
+                    "publishedAt": article.get("publishedAt", "Unknown"),
+                    "content": content,
+                    "summary": summary,
+                    "sentiment": sentiment,
+                    "score": score
+                })
+    return extracted_articles
+
+def extract_summarize_and_analyze_content_from_file(files):
+    extracted_articles = []
+    for file in files:
+        with open(file.name, "r", encoding="utf-8") as f:
+            content = f.read()
+            if content.strip():
+                summary = summarizer.generate_summary(content)
+                sentiment, score = analyze_sentiment.analyze_summary(summary)
+                extracted_articles.append({
+                    "title": "Custom File",
+                    "url": "N/A",
+                    "source": "Uploaded File",
+                    "author": "Unknown",
+                    "publishedAt": "Unknown",
+                    "content": content,
+                    "summary": summary,
+                    "sentiment": sentiment,
+                    "score": score
+                })
+    return extracted_articles
+
+def extract_summarize_and_analyze_content_from_urls(urls):
+    extracted_articles = []
+    for url in urls:
+        content, title = extract_news.extract_full_content(url)
+        if content:  # Only proceed if content is successfully extracted
+            summary = summarizer.generate_summary(content)
+            sentiment, score = analyze_sentiment.analyze_summary(summary)
+            extracted_articles.append({
+                "title": title if title else "Untitled Article",
+                "url": url,
+                "source": "External Link",
+                "author": "Unknown",
+                "publishedAt": "Unknown",
+                "content": content,
+                "summary": summary,
+                "sentiment": sentiment,
+                "score": score
+            })
+    return extracted_articles
+
+def display_clusters_as_columns(result, sentiment_filters=None):
+    df = result["dataframe"]
+    detected_topics = result.get("detected_topics", {})
+    df["sentiment"] = df["sentiment"].str.capitalize()
+
+    if sentiment_filters:
+        df = df[df["sentiment"].isin(sentiment_filters)]
+
+    if df.empty:
+        return ["### ⚠️ No matching articles."] + [""] * 4
+
+    clusters = df.groupby("cluster_label")
+    markdown_blocks = []
+
+    for cluster_label, articles in clusters:
+        cluster_md = f"### 🧩 Cluster {cluster_label}\n"
+        if cluster_label in detected_topics:
+            topics = detected_topics[cluster_label]
+            cluster_md += f"**Primary Topic:** {topics['primary_focus']}\n\n"
+            if topics["related_topics"]:
+                cluster_md += f"**Related Topics:** {', '.join(topics['related_topics'])}\n\n"
+        cluster_md += f"**Articles:** {len(articles)}\n\n"
+        for _, article in articles.iterrows():
+            cluster_md += (
+                f"#### 📰 {article['title']}\n"
+                f"- **Source:** {article['source']}\n"
+                f"- **Sentiment:** {article['sentiment']}\n"
+                f"<details><summary><strong>Summary</strong></summary>\n"
+                f"{article['summary']}\n"
+                f"</details>\n"
+                f"- [Read Full Article]({article['url']})\n\n"
+            )
+        
+        markdown_blocks.append(cluster_md)
+
+    while len(markdown_blocks) < 5:
+        markdown_blocks.append("")
+
+    return markdown_blocks[:5]
+
+def save_clustered_articles(df, topic):
+    if df.empty:
+        return None, None
+    csv_file = f"{topic.replace(' ', '_')}_clustered_articles.csv"
+    df.to_csv(csv_file, index=False)
+    return csv_file, None
+
+# ------------------ Pipeline Trigger ------------------
+
+def update_ui_with_columns(topic, files, urls, sentiment_filters):
+    extracted_articles = []
+
+    if topic.strip():
+        articles = fetch_content(topic)
+        if articles:
+            extracted_articles.extend(extract_summarize_and_analyze_articles(articles))
+
+    if files:
+        extracted_articles.extend(extract_summarize_and_analyze_content_from_file(files))
+
+    if urls:
+        url_list = [url.strip() for url in urls.split("\n") if url.strip()]
+        extracted_articles.extend(extract_summarize_and_analyze_content_from_urls(url_list))
+
+    if not extracted_articles:
+        return sentiment_filters, "### No content to display", "", "", "", "", None
+
+    df = pd.DataFrame(extracted_articles)
+    result = cluster_news.cluster_and_label_articles(df, content_column="content", summary_column="summary")
+    cluster_md_blocks = display_clusters_as_columns(result, sentiment_filters)
+    csv_file, _ = save_clustered_articles(result["dataframe"], topic or "batch_upload")
+
+    return sentiment_filters, *cluster_md_blocks, csv_file
+
+def clear_interface():
+    return (
+        "",                                 # topic_input
+        ["Positive", "Neutral", "Negative"],# sentiment_filter
+        gr.update(value=None),              # uploaded_files (reset file upload)
+        "",                                 # urls_input
+        "", "", "", "", "",                 # cluster columns 0–4
+        gr.update(value=None)               # csv_output (reset download file)
+    )
+
+
+# ------------------ Gradio UI ------------------
+
+with gr.Blocks(theme=gr.themes.Base(), css=".gr-markdown { margin: 10px; }") as demo:
+    
+    # Header Section
+    gr.Markdown("# 📰 Quick Pulse")
+    gr.Markdown("### AI-Powered News Summarization with Real-Time Sentiment and Topic Insights")
+    gr.Markdown(
+        "From headlines to insight, Quick Pulse summarizes news stories, captures emotional context, and clusters related topics to provide structured intelligence—faster than ever")
+
+    # Input Section
+    gr.Markdown("---")  # Horizontal line for separation
+    with gr.Accordion("🗞️ Latest Top Headlines", open=False):
+        latest_news_button = gr.Button("Fetch & Summarize Top 10 Headlines")
+
+    with gr.Row():
+        topic_input = gr.Textbox(label="Enter Topic", placeholder="e.g. climate change")
+        sentiment_filter = gr.CheckboxGroup(choices=["Positive", "Neutral", "Negative"], value=["Positive", "Neutral", "Negative"], label="Sentiment Filter")
+        csv_output = gr.File(label="📁 Download Clustered Digest CSV")
+
+    with gr.Accordion("📂 Upload Articles (.txt files)", open=False):
+        uploaded_files = gr.File(label="Upload .txt Files", file_types=[".txt"], file_count="multiple")
+
+    with gr.Accordion("🔗 Enter Multiple URLs", open=False):
+        urls_input = gr.Textbox(label="Enter URLs (newline separated)", lines=4)
+
+    with gr.Row():
+        submit_button = gr.Button(" Generate Digest")
+        clear_button = gr.Button(" Clear")
+
+    with gr.Row():
+        column_0 = gr.Markdown()
+        column_1 = gr.Markdown()
+        column_2 = gr.Markdown()
+        column_3 = gr.Markdown()
+        column_4 = gr.Markdown()
+
+    submit_button.click(
+        fn=update_ui_with_columns,
+        inputs=[topic_input, uploaded_files, urls_input, sentiment_filter],
+        outputs=[
+            sentiment_filter,
+            column_0, column_1, column_2, column_3, column_4,
+            csv_output
+        ]
+    )
+
+    latest_news_button.click(
+        fn=fetch_and_process_latest_news,
+        inputs=[sentiment_filter],
+        outputs=[
+            sentiment_filter,
+            column_0, column_1, column_2, column_3, column_4,
+            csv_output
+        ]
+    )
+
+    clear_button.click(
+    fn=clear_interface,
+    inputs=[],
+    outputs=[
+        topic_input,          # 1
+        sentiment_filter,     # 2
+        uploaded_files,       # 3
+        urls_input,           # 4
+        column_0,             # 5
+        column_1,             # 6
+        column_2,             # 7
+        column_3,             # 8
+        column_4,             # 9
+        csv_output            # 10
+    ]
+)
+
+
+
+if __name__ == "__main__":
+    demo.launch()