Update app.py

app.py

@@ -4,6 +4,8 @@ import pandas as pd
 import plotly.express as px
 import numpy as np  # for a tiny linear-forecast
 
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
 from search import get_news
 from llm import summarize
 from huggingface_nlp import analyze_sentiment, analyze_entities, extract_keywords
@@ -345,19 +347,46 @@ def export_briefing_pdf(topic: str, briefing_md: str, timestamp_str: str):
 
 # ---------------------- Gradio callbacks ----------------------
 def analyze_news(mode, preset_company, topic, days, k, entity_filter, sentiment_filter, fast_mode):
+    # Build query hint from mode
     query_hint = ONE_CLICK.get(mode, ONE_CLICK["General"])["query_hint"] if mode in ONE_CLICK else ""
 
-    # add preset company into topic if missing
+    # If a preset company is chosen and not already in the topic, append it
     if preset_company and preset_company.lower() not in (topic or "").lower():
         topic = f"{topic} {preset_company}".strip()
-    
+
+    # Run pipeline (fast mode skips entities/keywords + parallelizes)
     rows, df, mdf, rollup, briefing, press, jobs, ts = run_pipeline(
         topic, days, k, query_hint=query_hint, fast=bool(fast_mode)
     )
-    ...
-    return (header, cards_html, make_sentiment_chart(df), make_trend_chart(df),
-            make_forecast_chart(df), df, mdf if not mdf.empty else pd.DataFrame([{"note":"No per-article metrics yet"}]),
-            rollup, briefing, gr.update(choices=all_ents))
+
+    # Render cards with current filters
+    cards_html = render_cards(rows, entity_filter or None, sentiment_filter or None)
+
+    # Collect entities for the dropdown (limit to 50 unique)
+    all_ents = sorted(set(
+        e.strip()
+        for r in rows
+        for e in (r.get("Entities", "").split(", "))
+        if e.strip()
+    ))[:50]
+
+    # Header bar text
+    header = f"🗞️ NewsIntel — Data last updated: {ts}"
+
+    # Return in same order your .click() expects
+    return (
+        header,                        # header_bar
+        cards_html,                    # cards
+        make_sentiment_chart(df),      # plot_sent
+        make_trend_chart(df),          # plot_trend
+        make_forecast_chart(df),       # plot_forecast
+        df,                            # table
+        mdf if not mdf.empty else pd.DataFrame([{"note": "No per-article metrics yet"}]),  # per_article
+        rollup,                        # rollup
+        briefing,                      # briefing_md
+        gr.update(choices=all_ents)    # entity_filter (choices updated)
+    )
+
 
 # wiring: include fast_mode in inputs
 run_btn.click(
@@ -403,22 +432,24 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet", neutral_hue="slate"))
     </div>
     """)
 
+    # ---------- inputs ----------
     with gr.Row():
         mode  = gr.Dropdown(choices=list(ONE_CLICK.keys()), value="General", label="One-Click Mode")
         preset_company = gr.Dropdown(choices=H1B_TECH_PRESETS, label="Company Presets (H-1B Tech)", allow_custom_value=True)
-        topic  = gr.Textbox(label="Topic / Company",
-                            value="",               # was "NVIDIA"
+        topic  = gr.Textbox(label="Topic / Company", value="",  # <— was "NVIDIA"
                             placeholder="e.g., AMD, Healthcare AI, EV market India")
         days   = gr.Slider(1, 30, value=7, step=1, label="Lookback (days)")
-        k      = gr.Slider(3, 20, value=8, step=1, label="Articles")
+        k      = gr.Slider(3, 12, value=4, step=1, label="Articles")  # faster default
         fast_mode = gr.Checkbox(value=True, label="⚡ Fast mode (skip Entities & Key Phrases)")
 
     with gr.Row():
         entity_filter = gr.Dropdown(choices=[], label="Filter by Mentioned Company/Person", value=None)
         sentiment_filter = gr.Dropdown(choices=["ALL","POSITIVE","NEUTRAL","NEGATIVE","MIXED"], value="ALL", label="Sentiment filter")
 
+    # run button MUST be defined before we call run_btn.click(...)
     run_btn = gr.Button("Run Analysis", variant="primary")
 
+    # ---------- outputs ----------
     header_bar = gr.Markdown(value="🗞️ NewsIntel — Data last updated: —")
     with gr.Tab("Insights"):
         tip_md = gr.Markdown("💡 **Tip:** *Entities* are detected names of companies/people/places (e.g., “TSMC”, “ARM”). Use the filters to focus the feed.")
@@ -445,20 +476,18 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet", neutral_hue="slate"))
             email_btn = gr.Button("Email Weekly Briefing (SES)")
             email_status = gr.Markdown()
 
-    # Wiring
+    # ---------- helpers & wiring ----------
     def _apply_mode(m, current_topic):
         cfg = ONE_CLICK.get(m, ONE_CLICK["General"])
-        # only set the topic if the box is empty
-        new_value = current_topic or cfg.get("topic", "")
-        return gr.update(value=new_value)
+        return gr.update(value=current_topic or cfg.get("topic",""))
 
     mode.change(_apply_mode, inputs=[mode, topic], outputs=[topic])
-
     preset_company.change(lambda x: x or "", inputs=preset_company, outputs=topic)
 
+    # include fast_mode in inputs
     run_btn.click(
         analyze_news,
-        inputs=[mode, preset_company, topic, days, k, entity_filter, sentiment_filter],
+        inputs=[mode, preset_company, topic, days, k, entity_filter, sentiment_filter, fast_mode],
         outputs=[header_bar, cards, plot_sent, plot_trend, plot_forecast, table, per_article, rollup, briefing_md, entity_filter]
     ).then(
         lambda: datetime.datetime.now().strftime("%b %d, %Y %I:%M %p"),
@@ -468,6 +497,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet", neutral_hue="slate"))
     export_btn.click(export_cb, inputs=[topic, briefing_md, timestamp_str], outputs=[export_html, export_pdf, s3_links])
     email_btn.click(email_weekly_cb, inputs=[topic, weekly_email, briefing_md, timestamp_str], outputs=[email_status])
 
+
 if __name__ == "__main__":
     print("🚀 Launching NewsIntel (light-only UI + caching + one-click modes + forecast)")
     demo.launch()