Change layout and charts

app.py

@@ -1,398 +1,397 @@
-import gradio as gr
-import pandas as pd
-import requests
-from bs4 import BeautifulSoup
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-from datetime import datetime, timedelta
-import yfinance as yf
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-import io
-import base64
-import html
-
-# -----------------------------
-# LOAD LOCAL TICKERS
-# -----------------------------
-tickers = pd.read_csv("tickers.csv")
-
-def get_company_name(symbol: str):
-    symbol = symbol.upper().strip()
-    row = tickers[tickers["Symbol"] == symbol]
-    if not row.empty:
-        return True, row.iloc[0]["Symbol"], row.iloc[0]["Company"]
-
-    try:
-        ticker = yf.Ticker(symbol + ".NS")
-        info = ticker.info
-        if "longName" in info:
-            return True, symbol, info["longName"]
-    except Exception:
-        pass
-
-    return False, None, None
-
-# -----------------------------
-# FETCH NEWS
-# -----------------------------
-def fetch_news(query, max_items=50):
-    url = f"https://news.google.com/rss/search?q={query}+stock&hl=en-IN&gl=IN&ceid=IN:en"
-    try:
-        r = requests.get(url, timeout=5)
-        r.raise_for_status()
-    except Exception:
-        return [], 0
-
-    soup = BeautifulSoup(r.text, "lxml-xml")
-    items = soup.find_all("item")
-
-    news_list = []
-    for item in items[:max_items]:
-        news_list.append({
-            "title": item.title.text,
-            "link": item.link.text,
-            "published": item.pubDate.text,
-        })
-
-    return news_list, len(items)
-
-# -----------------------------
-# DATE PARSER
-# -----------------------------
-def parse_date(date_str):
-    if not date_str: 
-        return None
-    fmts = ["%a, %d %b %Y %H:%M:%S %Z", "%a, %d %b %Y %H:%M:%S %z"]
-    for fmt in fmts:
-        try:
-            return datetime.strptime(date_str, fmt)
-        except:
-            pass
-    return None
-
-# -----------------------------
-# FILTER NEWS BY PERIOD
-# -----------------------------
-def filter_news_by_period(news_list, period_days=7):
-    cutoff = datetime.utcnow() - timedelta(days=period_days)
-    filtered = []
-    for item in news_list:
-        dt = parse_date(item.get("published", ""))
-        if dt and dt.replace(tzinfo=None) >= cutoff:
-            filtered.append(item)
-    return filtered
-
-# -----------------------------
-# LOAD FINBERT
-# -----------------------------
-tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
-model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
-labels = [model.config.id2label[i] for i in range(model.config.num_labels)]
-
-def analyze_sentiment_batch(texts):
-    batch = tokenizer(texts, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
-        output = model(**batch)
-        probs = torch.nn.functional.softmax(output.logits, dim=1)
-
-    results = []
-    for p in probs:
-        results.append({
-            "positive": float(p[labels.index("positive")]),
-            "neutral": float(p[labels.index("neutral")]),
-            "negative": float(p[labels.index("negative")]),
-        })
-    return results
-
-# -----------------------------
-# MAIN PIPELINE
-# -----------------------------
-def run_pipeline(user_input, period_option, max_news):
-
-    # Extract symbol if user selected "SYMBOL - Company"
-    if " - " in user_input:
-        raw_symbol = user_input.split(" - ")[0].strip()
-    else:
-        raw_symbol = user_input.strip()
-
-    period_map = {"Last 7 days": 7, "Last 10 days": 10, "Last 1 month": 30}
-    period_days = period_map.get(period_option, 7)
-
-    valid, symbol, company = get_company_name(raw_symbol)
-    if not valid:
-        return f"❌ '{user_input}' is not a valid NSE stock symbol.", "", "", "", "", "", ""
-
-    # Fetch news
-    query = company + " stock"
-    news, total_items = fetch_news(query, max_items=int(max_news))
-    news = filter_news_by_period(news, period_days=period_days)
-
-    fetched_count = min(total_items, int(max_news))
-
-    if len(news) == 0:
-        return f"No news found for {company} in {period_option}", "", "", "", "", "", ""
-
-    info_msg = f"**Showing {len(news)} headlines from the last {period_days} days (fetched {fetched_count} / requested {max_news}).**"
-
-    # Sentiment
-    texts = [n["title"] for n in news]
-    sentiments = analyze_sentiment_batch(texts)
-
-    results = []
-    counts = {"positive": 0, "neutral": 0, "negative": 0}
-    overall_sums = {"positive": 0.0, "neutral": 0.0, "negative": 0.0}
-    weighted_counts = {"positive": 0.0, "neutral": 0.0, "negative": 0.0}
-    date_sentiments = {}
-    date_counts = {}
-
-    for item, sent in zip(news, sentiments):
-        pos, neu, neg = sent["positive"], sent["neutral"], sent["negative"]
-        overall = round(pos - neg, 3)
-
-        pred = max(["positive", "neutral", "negative"], key=lambda k: sent[k])
-        counts[pred] += 1
-        overall_sums[pred] += overall
-
-        for k in ["positive", "neutral", "negative"]:
-            weighted_counts[k] += sent[k]
-
-        dt = parse_date(item["published"])
-        if dt:
-            dkey = dt.date().isoformat()
-            date_sentiments.setdefault(dkey, []).append(overall)
-            date_counts.setdefault(dkey, []).append(pred)
-
-        results.append({
-            "headline": item["title"],
-            "positive": round(pos, 3),
-            "neutral": round(neu, 3),
-            "negative": round(neg, 3),
-            "overall": overall,
-            "published": item["published"],
-            "link": item["link"]
-        })
-
-    results.sort(key=lambda x: parse_date(x["published"]), reverse=True)
-
-    avg_overall = {k: (overall_sums[k] / counts[k] if counts[k] > 0 else 0.0)
-                   for k in counts}
-
-    # -----------------------------
-    # SUMMARY
-    # -----------------------------
-    summary = f"""
-### 📊 Sentiment Summary for {company} ({symbol}) — {period_option}
-
-| Sentiment | Count | % | Avg Sentiment (Overall) | Weighted Count |
-|----------|-------|------|--------------|-----------|
-| 😊 Positive | {counts['positive']} | {counts['positive']/len(news)*100:.1f}% | {avg_overall['positive']:.2f} | {weighted_counts['positive']:.2f} |
-| 😐 Neutral | {counts['neutral']} | {counts['neutral']/len(news)*100:.1f}% | {avg_overall['neutral']:.2f} | {weighted_counts['neutral']:.2f} |
-| 😞 Negative | {counts['negative']} | {counts['negative']/len(news)*100:.1f}% | {avg_overall['negative']:.2f} | {weighted_counts['negative']:.2f} |
-
-**Headlines in period:** {len(news)}  
-"""
-
-    # -----------------------------
-    # HEADLINES TABLE
-    # -----------------------------
-    table = """
-    <table style='width:100%; border-collapse: collapse;'>
-    <tr style='background-color:#f2f2f2'>
-        <th>Published</th>
-        <th>Headline</th>
-        <th>Positive</th>
-        <th>Neutral</th>
-        <th>Negative</th>
-        <th>Overall (POS-NEG)</th>
-        <th>Link</th>
-    </tr>
-    """
-
-    for i, r in enumerate(results):
-        row_color = "#ffffff" if i % 2 == 0 else "#f9f9f9"
-
-        # Determine highest sentiment
-        max_sent = max(["positive", "neutral", "negative"], key=lambda k: r[k])
-
-        # Colors
-        color_map = {"positive": "green", "neutral": "gray", "negative": "red"}
-
-        # Helper: build cell HTML
-        def cell_html(value, sent_type):
-            if max_sent == sent_type:
-                return f"<td style='color:{color_map[sent_type]}; font-weight:bold'>{value}</td>"
-            else:
-                return f"<td>{value}</td>"
-
-        color = "green" if r["overall"] > 0 else "red" if r["overall"] < 0 else "black"
-
-        table += f"<tr style='background-color:{row_color};'>"
-        table += f"<td>{r['published']}</td>"
-        table += f"<td>{html.escape(r['headline'])}</td>"
-        table += cell_html(r['positive'], "positive")
-        table += cell_html(r['neutral'], "neutral")
-        table += cell_html(r['negative'], "negative")
-        table += f"<td style='color:{color}; font-weight:bold'>{r['overall']}</td>"
-        table += f"<td><a href='{r['link']}' target='_blank'>Open</a></td>"
-        table += "</tr>"
-
-    table += "</table>"
-
-    # -----------------------------
-    # CSV DOWNLOAD
-    # -----------------------------
-    df = pd.DataFrame(results)
-    csv_buf = io.StringIO()
-    df.to_csv(csv_buf, index=False)
-    csv_data = "data:text/csv;base64," + base64.b64encode(csv_buf.getvalue().encode()).decode()
-    csv_link_html = f"<a href='{csv_data}' download='{symbol}_news_sentiment.csv'>⬇️ Download CSV</a>"
-
-    # -----------------------------
-    # CHARTS
-    # -----------------------------
-    chart_counts_html = chart_sentiment_html = chart_price_sentiment_html = ""
-
-    if date_sentiments:
-        dates = sorted(date_sentiments.keys())
-        avg_sentiments = [sum(date_sentiments[d])/len(date_sentiments[d]) for d in dates]
-        pos_counts = [date_counts[d].count("positive") for d in dates]
-        neu_counts = [date_counts[d].count("neutral") for d in dates]
-        neg_counts = [date_counts[d].count("negative") for d in dates]
-
-        # Chart 1
-        fig1, ax1 = plt.subplots(figsize=(6, 4))
-        ax1.bar(dates, pos_counts, color="green")
-        ax1.bar(dates, neu_counts, bottom=pos_counts, color="gray")
-        ax1.bar(dates, neg_counts, bottom=[p + n for p, n in zip(pos_counts, neu_counts)], color="red")
-        ax1.set_xlabel("")
-        ax1.set_title("Daily Headline Counts")
-        ax1.tick_params(axis='x', rotation=60, labelsize=7)
-        plt.tight_layout()
-        buf1 = io.BytesIO()
-        fig1.savefig(buf1, format="png")
-        buf1.seek(0)
-        chart_counts_html = f"<img src='data:image/png;base64,{base64.b64encode(buf1.read()).decode()}' style='width:100%; max-width:700px;'/>"
-        plt.close(fig1)
-
-        # Chart 2
-        fig2, ax2 = plt.subplots(figsize=(6, 4))
-        colors = ["green" if x > 0 else "red" if x < 0 else "gray" for x in avg_sentiments]
-        ax2.bar(dates, avg_sentiments, color=colors)
-        ax2.axhline(0, color="black", linestyle="--")
-        ax2.set_title("Daily Sentiment Trend")
-        ax2.tick_params(axis='x', rotation=60, labelsize=7)
-        plt.tight_layout()
-        buf2 = io.BytesIO()
-        fig2.savefig(buf2, format="png")
-        buf2.seek(0)
-        chart_sentiment_html = f"<img src='data:image/png;base64,{base64.b64encode(buf2.read()).decode()}' style='width:100%; max-width:700px;'/>"
-        plt.close(fig2)
-
-        # Chart 3 - Stock Price + Sentiment
-        try:
-            ticker_data = yf.Ticker(symbol + ".NS").history(period=f"{period_days}d")
-
-            if not ticker_data.empty:
-                fig3, ax3 = plt.subplots(figsize=(6, 4))
-                ax3.plot(ticker_data.index, ticker_data['Close'], color="blue", label="Close Price")
-
-                sentiment_dates = [pd.to_datetime(d) for d in dates]
-                ax3_twin = ax3.twinx()
-                ax3_twin.plot(sentiment_dates, avg_sentiments, color="orange", marker="o", label="Sentiment Score")
-
-                lines1, labels1 = ax3.get_legend_handles_labels()
-                lines2, labels2 = ax3_twin.get_legend_handles_labels()
-                ax3.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
-
-                ax3.set_title("Daily Stock Price + Sentiment Trend")
-                ax3.xaxis.set_major_formatter(mdates.DateFormatter("%d-%b"))
-                fig3.autofmt_xdate()
-
-                plt.tight_layout()
-                buf3 = io.BytesIO()
-                fig3.savefig(buf3, format="png")
-                buf3.seek(0)
-                chart_price_sentiment_html = f"<img src='data:image/png;base64,{base64.b64encode(buf3.read()).decode()}' style='width:100%; max-width:700px;'/>"
-                plt.close(fig3)
-
-        except Exception:
-            chart_price_sentiment_html = ""
-
-    # -----------------------------
-    # KEY METRICS SUMMARY (Markdown)
-    # -----------------------------
-    metrics_summary_text = """
-### 📌 Key Metrics Summary
-
-| Metric | Description |
-|--------|------------|
-| **Count** | Number of headlines predicted as Positive / Neutral / Negative. |
-| **%** | Percentage of headlines in each sentiment category. |
-| **Avg Sentiment (Overall)** | Average **overall score** for headlines in this category. Calculated as **positive probability − negative probability** per headline, then averaged. |
-| **Weighted Count** | Sum of the raw sentiment probabilities for each category across all headlines. Provides a “confidence-weighted” measure of sentiment dominance. |
-| **Overall Score (per headline)** | `positive − negative` probability. Shows whether the headline is more positive or negative. |
-| **Dominant Sentiment (per headline)** | The sentiment with the **highest probability** among positive, neutral, or negative. Highlighted in the table. |
-
-**Charts:**
-- **Daily Headline Counts**: Shows how many headlines per day fall into each sentiment.  
-- **Daily Sentiment Trend**: Shows average overall sentiment per day (positive − negative).  
-- **Stock Price + Sentiment Trend**: Plots stock closing price alongside daily sentiment for easy correlation.  
-"""
-
-    return (
-        summary,
-        info_msg,
-        chart_counts_html,
-        chart_sentiment_html,
-        table + "<br>" + csv_link_html,
-        chart_price_sentiment_html,
-        metrics_summary_text
-    )
-
-# -----------------------------
-# GRADIO UI
-# -----------------------------
-with gr.Blocks(title="Indian Stock Market Sentiment Analyzer") as ui:
-
-    gr.Markdown("<h1 style='text-align:center;'>🇮🇳 Indian Stock Market Sentiment Analyzer</h1>")
-    gr.Markdown("<p style='text-align:center;'>Enter an NSE/BSE stock symbol. The app uses FinBERT + Google News to generate sentiment analysis of recent headlines.</p>")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            symbol_in = gr.Textbox(label="Enter Stock Symbol (e.g., RELIANCE, TCS)")
-            period_in = gr.Dropdown(
-                ["Last 7 days", "Last 10 days", "Last 1 month"],
-                value="Last 7 days",
-                label="Select Period"
-            )
-            max_news_in = gr.Slider(
-                minimum=20, maximum=100, step=1, value=50,
-                label="Number of Headlines to Fetch"
-            )
-            btn = gr.Button("Analyze")
-
-        with gr.Column(scale=2):
-            summary_out = gr.Markdown()
-            info_out = gr.Markdown()
-
-    with gr.Row():
-        chart1_out = gr.HTML(label="Daily Headline Counts")
-        chart2_out = gr.HTML(label="Daily Sentiment Trend")
-        chart3_out = gr.HTML(label="Daily Stock Price + Sentiment Trend")
-
-    with gr.Row():
-        table_out = gr.HTML(label="Headlines Table")
-
-    # Add metrics summary in a separate row to make it clearly visible
-    with gr.Row():
-        metrics_summary_out = gr.Markdown(label="Key Metrics Summary")
-
-    btn.click(
-        run_pipeline,
-        inputs=[symbol_in, period_in, max_news_in],
-        outputs=[summary_out, info_out, chart1_out, chart2_out, table_out, chart3_out, metrics_summary_out]
-    )
-
-ui.launch(share=True)
-
+import gradio as gr
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from datetime import datetime, timedelta
+import yfinance as yf
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import io
+import base64
+import html
+
+# -----------------------------
+# LOAD LOCAL TICKERS
+# -----------------------------
+tickers = pd.read_csv("tickers.csv")
+
+def get_company_name(symbol: str):
+    symbol = symbol.upper().strip()
+    row = tickers[tickers["Symbol"] == symbol]
+    if not row.empty:
+        return True, row.iloc[0]["Symbol"], row.iloc[0]["Company"]
+
+    try:
+        ticker = yf.Ticker(symbol + ".NS")
+        info = ticker.info
+        if "longName" in info:
+            return True, symbol, info["longName"]
+    except Exception:
+        pass
+
+    return False, None, None
+
+# -----------------------------
+# FETCH NEWS
+# -----------------------------
+def fetch_news(query, max_items=50):
+    url = f"https://news.google.com/rss/search?q={query}+stock&hl=en-IN&gl=IN&ceid=IN:en"
+    try:
+        r = requests.get(url, timeout=5)
+        r.raise_for_status()
+    except Exception:
+        return [], 0
+
+    soup = BeautifulSoup(r.text, "lxml-xml")
+    items = soup.find_all("item")
+
+    news_list = []
+    for item in items[:max_items]:
+        news_list.append({
+            "title": item.title.text,
+            "link": item.link.text,
+            "published": item.pubDate.text,
+        })
+
+    return news_list, len(items)
+
+# -----------------------------
+# DATE PARSER
+# -----------------------------
+def parse_date(date_str):
+    if not date_str: 
+        return None
+    fmts = ["%a, %d %b %Y %H:%M:%S %Z", "%a, %d %b %Y %H:%M:%S %z"]
+    for fmt in fmts:
+        try:
+            return datetime.strptime(date_str, fmt)
+        except:
+            pass
+    return None
+
+# -----------------------------
+# FILTER NEWS BY PERIOD
+# -----------------------------
+def filter_news_by_period(news_list, period_days=7):
+    cutoff = datetime.utcnow() - timedelta(days=period_days)
+    filtered = []
+    for item in news_list:
+        dt = parse_date(item.get("published", ""))
+        if dt and dt.replace(tzinfo=None) >= cutoff:
+            filtered.append(item)
+    return filtered
+
+# -----------------------------
+# LOAD FINBERT
+# -----------------------------
+tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
+model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
+labels = [model.config.id2label[i] for i in range(model.config.num_labels)]
+
+def analyze_sentiment_batch(texts):
+    batch = tokenizer(texts, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        output = model(**batch)
+        probs = torch.nn.functional.softmax(output.logits, dim=1)
+
+    results = []
+    for p in probs:
+        results.append({
+            "positive": float(p[labels.index("positive")]),
+            "neutral": float(p[labels.index("neutral")]),
+            "negative": float(p[labels.index("negative")]),
+        })
+    return results
+
+# -----------------------------
+# MAIN PIPELINE
+# -----------------------------
+def run_pipeline(user_input, period_option, max_news):
+
+    # Extract symbol if user selected "SYMBOL - Company"
+    if " - " in user_input:
+        raw_symbol = user_input.split(" - ")[0].strip()
+    else:
+        raw_symbol = user_input.strip()
+
+    period_map = {"Last 7 days": 7, "Last 10 days": 10, "Last 1 month": 30}
+    period_days = period_map.get(period_option, 7)
+
+    valid, symbol, company = get_company_name(raw_symbol)
+    if not valid:
+        return f"❌ '{user_input}' is not a valid NSE stock symbol.", "", "", "", "", "", ""
+
+    # Fetch news
+    query = company + " stock"
+    news, total_items = fetch_news(query, max_items=int(max_news))
+    news = filter_news_by_period(news, period_days=period_days)
+
+    fetched_count = min(total_items, int(max_news))
+
+    if len(news) == 0:
+        return f"No news found for {company} in {period_option}", "", "", "", "", "", ""
+
+    info_msg = f"**Found {len(news)} headlines from the last {period_days} days (fetched {fetched_count} / requested {max_news}).**"
+
+    # Sentiment
+    texts = [n["title"] for n in news]
+    sentiments = analyze_sentiment_batch(texts)
+
+    results = []
+    counts = {"positive": 0, "neutral": 0, "negative": 0}
+    overall_sums = {"positive": 0.0, "neutral": 0.0, "negative": 0.0}
+    weighted_counts = {"positive": 0.0, "neutral": 0.0, "negative": 0.0}
+    date_sentiments = {}
+    date_counts = {}
+
+    for item, sent in zip(news, sentiments):
+        pos, neu, neg = sent["positive"], sent["neutral"], sent["negative"]
+        overall = round(pos - neg, 3)
+
+        pred = max(["positive", "neutral", "negative"], key=lambda k: sent[k])
+        counts[pred] += 1
+        overall_sums[pred] += overall
+
+        for k in ["positive", "neutral", "negative"]:
+            weighted_counts[k] += sent[k]
+
+        dt = parse_date(item["published"])
+        if dt:
+            dkey = dt.date().isoformat()
+            date_sentiments.setdefault(dkey, []).append(overall)
+            date_counts.setdefault(dkey, []).append(pred)
+
+        results.append({
+            "headline": item["title"],
+            "positive": round(pos, 3),
+            "neutral": round(neu, 3),
+            "negative": round(neg, 3),
+            "overall": overall,
+            "published": item["published"],
+            "link": item["link"]
+        })
+
+    results.sort(key=lambda x: parse_date(x["published"]), reverse=True)
+
+    # -----------------------------
+    # SUMMARY
+    # -----------------------------
+    summary = f"""
+### 📊 Sentiment Summary for {company} ({symbol}) — {period_option}
+
+| Sentiment | Count | Weighted Count |
+|----------|-------|-----------|
+| 😊 Positive | {counts['positive']} | {weighted_counts['positive']:.2f} |
+| 😐 Neutral | {counts['neutral']} | {weighted_counts['neutral']:.2f} |
+| 😞 Negative | {counts['negative']} | {weighted_counts['negative']:.2f} |
+| Total | {len(news)} | {len(news)} |
+
+"""
+
+    # -----------------------------
+    # HEADLINES TABLE
+    # -----------------------------
+    table = """
+    <table style='width:100%; border-collapse: collapse;'>
+    <tr style='background-color:#f2f2f2'>
+        <th>Published</th>
+        <th>Headline</th>
+        <th>Positive</th>
+        <th>Neutral</th>
+        <th>Negative</th>
+        <th>Sentiment Score<br>(POS-NEG)</th>
+        <th>Link</th>
+    </tr>
+    """
+
+    for i, r in enumerate(results):
+        row_color = "#ffffff" if i % 2 == 0 else "#f9f9f9"
+
+        # Determine highest sentiment
+        max_sent = max(["positive", "neutral", "negative"], key=lambda k: r[k])
+
+        # Colors
+        color_map = {"positive": "green", "neutral": "gray", "negative": "red"}
+
+        # Helper: build cell HTML
+        def cell_html(value, sent_type):
+            if max_sent == sent_type:
+                return f"<td style='color:{color_map[sent_type]}; font-weight:bold'>{value}</td>"
+            else:
+                return f"<td>{value}</td>"
+
+        color = "green" if r["overall"] > 0 else "red" if r["overall"] < 0 else "black"
+
+        table += f"<tr style='background-color:{row_color};'>"
+        table += f"<td>{r['published']}</td>"
+        table += f"<td>{html.escape(r['headline'])}</td>"
+        table += cell_html(r['positive'], "positive")
+        table += cell_html(r['neutral'], "neutral")
+        table += cell_html(r['negative'], "negative")
+        table += f"<td style='color:{color}; font-weight:bold'>{r['overall']}</td>"
+        table += f"<td><a href='{r['link']}' target='_blank'>Open</a></td>"
+        table += "</tr>"
+
+    table += "</table>"
+
+    # -----------------------------
+    # CSV DOWNLOAD
+    # -----------------------------
+    df = pd.DataFrame(results)
+    csv_buf = io.StringIO()
+    df.to_csv(csv_buf, index=False)
+    csv_data = "data:text/csv;base64," + base64.b64encode(csv_buf.getvalue().encode()).decode()
+    csv_link_html = f"<a href='{csv_data}' download='{symbol}_news_sentiment.csv'>⬇️ Download CSV</a>"
+
+    # -----------------------------
+    # CHARTS
+    # -----------------------------
+    chart_counts_html = chart_sentiment_html = chart_price_sentiment_html = ""
+
+    if date_sentiments:
+        dates = sorted(date_sentiments.keys())
+        avg_sentiments = [sum(date_sentiments[d])/len(date_sentiments[d]) for d in dates]
+        pos_counts = [date_counts[d].count("positive") for d in dates]
+        neu_counts = [date_counts[d].count("neutral") for d in dates]
+        neg_counts = [date_counts[d].count("negative") for d in dates]
+
+        # Chart 1
+        fig1, ax1 = plt.subplots(figsize=(6, 3))
+        ax1.bar(dates, pos_counts, color="green")
+        ax1.bar(dates, neu_counts, bottom=pos_counts, color="gray")
+        ax1.bar(dates, neg_counts, bottom=[p + n for p, n in zip(pos_counts, neu_counts)], color="red")
+        ax1.set_xlabel("")
+        ax1.set_title("Daily Headline Counts")
+        ax1.tick_params(axis='x', rotation=60, labelsize=7)
+        plt.tight_layout()
+        buf1 = io.BytesIO()
+        fig1.savefig(buf1, format="png")
+        buf1.seek(0)
+        chart_counts_html = f"<img src='data:image/png;base64,{base64.b64encode(buf1.read()).decode()}' style='width:100%; max-width:700px;'/>"
+        plt.close(fig1)
+
+        # Chart 2
+        fig2, ax2 = plt.subplots(figsize=(6, 3))
+        colors = ["green" if x > 0 else "red" if x < 0 else "gray" for x in avg_sentiments]
+        ax2.bar(dates, avg_sentiments, color=colors)
+        ax2.axhline(0, color="black", linestyle="--")
+        ax2.set_title("Daily Sentiment Trend")
+        ax2.tick_params(axis='x', rotation=60, labelsize=7)
+        plt.tight_layout()
+        buf2 = io.BytesIO()
+        fig2.savefig(buf2, format="png")
+        buf2.seek(0)
+        chart_sentiment_html = f"<img src='data:image/png;base64,{base64.b64encode(buf2.read()).decode()}' style='width:100%; max-width:700px;'/>"
+        plt.close(fig2)
+
+        # Chart 3 - Stock Price + Sentiment
+        try:
+            ticker_data = yf.Ticker(symbol + ".NS").history(period=f"{period_days}d")
+
+            if not ticker_data.empty:
+                fig3, ax3 = plt.subplots(figsize=(6, 3))
+                ax3.plot(ticker_data.index, ticker_data['Close'], color="blue", marker="o", label="Close Price")
+
+                sentiment_dates = [pd.to_datetime(d) for d in dates]
+                ax3_twin = ax3.twinx()
+                ax3_twin.plot(sentiment_dates, avg_sentiments, color="orange", marker="o", label="Sentiment Score")
+
+                lines1, labels1 = ax3.get_legend_handles_labels()
+                lines2, labels2 = ax3_twin.get_legend_handles_labels()
+                ax3.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
+
+                ax3.set_title("Daily Stock Price + Sentiment Trend")
+                ax3.xaxis.set_major_formatter(mdates.DateFormatter("%d-%b"))
+                fig3.autofmt_xdate()
+
+                plt.tight_layout()
+                buf3 = io.BytesIO()
+                fig3.savefig(buf3, format="png")
+                buf3.seek(0)
+                chart_price_sentiment_html = f"<img src='data:image/png;base64,{base64.b64encode(buf3.read()).decode()}' style='width:100%; max-width:700px;'/>"
+                plt.close(fig3)
+
+        except Exception:
+            chart_price_sentiment_html = ""
+
+    # -----------------------------
+    # KEY METRICS SUMMARY (Markdown)
+    # -----------------------------
+    metrics_summary_text = """
+### 📌 Key Metrics Summary
+
+| Metric | Description |
+|--------|------------|
+| **Count** | Number of headlines predicted as Positive / Neutral / Negative. |
+| **Weighted Count** | Sum of the raw sentiment probabilities for each category across all headlines. Provides a “confidence-weighted” measure of sentiment dominance. |
+| **Sentiment Score (per headline)** | `positive − negative` probability. Shows whether the headline is more positive or negative. |
+| **Dominant Sentiment (per headline)** | The sentiment with the **highest probability** among positive, neutral, or negative. Highlighted in the table. |
+
+**Charts:**
+- **Daily Headline Counts**: Shows how many headlines per day fall into each sentiment.  
+- **Daily Sentiment Trend**: Shows average overall sentiment per day (positive − negative).  
+- **Daily Stock Price + Sentiment Trend**: Plots stock closing price alongside daily sentiment for easy correlation.  
+"""
+
+    return (
+        summary,
+        info_msg,
+        chart_counts_html,
+        chart_sentiment_html,
+        table + "<br>" + csv_link_html,
+        chart_price_sentiment_html,
+        metrics_summary_text
+    )
+
+# -----------------------------
+# GRADIO UI
+# -----------------------------
+with gr.Blocks(title="Indian Stock Market Sentiment Analyzer") as ui:
+
+    gr.Markdown("<h1 style='text-align:center;'>🇮🇳 Indian Stock Market Sentiment Analyzer</h1>")
+    gr.Markdown("<p style='text-align:center;'>Enter an NSE/BSE stock symbol. The app uses FinBERT + Google News to generate sentiment analysis of recent headlines.</p>")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            symbol_in = gr.Textbox(label="Enter Stock Symbol (e.g., RELIANCE, TCS)")
+        with gr.Column(scale=1):
+            period_in = gr.Dropdown(
+                ["Last 7 days", "Last 10 days", "Last 1 month"],
+                value="Last 7 days",
+                label="Select Period"
+            )
+        with gr.Column(scale=1):
+            max_news_in = gr.Slider(
+                minimum=20, maximum=100, step=1, value=50,
+                label="Number of Headlines to Fetch"
+            )
+            btn = gr.Button("Analyze")
+
+    with gr.Row():
+        info_out = gr.Markdown()
+
+    with gr.Row():
+        summary_out = gr.Markdown()
+        chart1_out = gr.HTML(label="Daily Headline Counts")
+        
+    with gr.Row():
+        chart2_out = gr.HTML(label="Daily Sentiment Trend")
+        chart3_out = gr.HTML(label="Daily Stock Price + Sentiment Trend")
+
+    with gr.Row():
+        table_out = gr.HTML(label="Headlines Table")
+
+    # Add metrics summary in a separate row to make it clearly visible
+    with gr.Row():
+        metrics_summary_out = gr.Markdown(label="Key Metrics Summary")
+
+    btn.click(
+        run_pipeline,
+        inputs=[symbol_in, period_in, max_news_in],
+        outputs=[summary_out, info_out, chart1_out, chart2_out, table_out, chart3_out, metrics_summary_out]
+    )
+
+ui.launch()
+