Update app.py

app.py

@@ -1,401 +1,399 @@
-import os
-import time
-import cv2
-import numpy as np
-import tensorflow as tf
-import gradio as gr
-import plotly.graph_objects as go
-import matplotlib.pyplot as plt
-from fpdf import FPDF
-from PIL import Image
-
-# ===============================
-# 1. Load Model
-# ===============================
-MODEL_PATH = "fer_surprise_softmax.h5"
-model = tf.keras.models.load_model(MODEL_PATH, compile=False)
-
-IMG_SIZE = (96, 96)
-CLASS_NAMES = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
-SURPRISE_IDX = CLASS_NAMES.index("surprise")
-
-# ===============================
-# 2. Face Detector
-# ===============================
-face_cascade = cv2.CascadeClassifier(
-    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
-)
-
-# ===============================
-# 3. State Storage
-# ===============================
-events = []           # surprise events (for Top 3)
-surprise_history = [] # (time, p_surprise) for timeline
-start_time = None     # stream start time
-MIN_EVENT_GAP = 1.0   # min seconds between events
-
-# Session stats
-frames_with_face = 0  # how many frames had a detected face
-max_p_surprise = 0.0  # maximum P(surprise) observed in the session
-
-# ===============================
-# 4. Utility: Time Formatting
-# ===============================
-def format_time(seconds: float) -> str:
-    minutes = int(seconds // 60)
-    sec = int(seconds % 60)
-    return f"{minutes:02d}:{sec:02d}"
-
-
-# ===============================
-# 5. Real-time Frame Processing
-# ===============================
-def detect_surprise(frame, threshold):
-
-    global events, start_time, surprise_history
-    global frames_with_face, max_p_surprise
-
-    if frame is None:
-        stats_text = (
-            "### Session Stats\n"
-            "- Session duration: 00:00\n"
-            f"- Current threshold: {threshold:.2f}\n"
-            "- Frames with face detected: 0\n"
-            "- Surprise events detected: 0\n"
-            "- Max P(surprise): 0.00\n"
-        )
-        return None, {"Error": 1.0}, None, stats_text
-
-    if start_time is None:
-        start_time = time.time()
-        surprise_history = []
-        events = []
-        frames_with_face = 0
-        max_p_surprise = 0.0
-
-    current_time = time.time() - start_time
-
-    frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-    gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)
-
-    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
-
-    # 변경된 기본 라벨: 얼굴 미검출 시 조명/각도 안내
-    label = "NO FACE - Try brighter lighting or adjust angle"
-    color = (0, 255, 255)
-    probs_dict = {}
-
-    if len(faces) > 0:
-        frames_with_face += 1
-        x, y, w, h = sorted(faces, key=lambda r: r[2] * r[3], reverse=True)[0]
-        roi = frame_bgr[y:y+h, x:x+w]
-
-        rgb = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
-        resized = cv2.resize(rgb, IMG_SIZE)
-        inp = resized.astype("float32") / 255.0
-        inp = np.expand_dims(inp, axis=0)
-
-        probs = model.predict(inp, verbose=0)[0]
-        p_surprise = float(probs[SURPRISE_IDX])
-
-        if p_surprise > max_p_surprise:
-            max_p_surprise = p_surprise
-
-        probs_dict = {
-            cls: float(p) for cls, p in zip(CLASS_NAMES, probs)
-        }
-
-        surprise_history.append({
-            "time": current_time,
-            "score": p_surprise,
-        })
-
-        # -------- Top3 detection logic --------
-        if p_surprise >= threshold:
-            if len(events) == 0:
-                events.append({
-                    "time": current_time,
-                    "score": p_surprise,
-                    "frame": frame.copy()
-                })
-            else:
-                dt = current_time - events[-1]["time"]
-                if dt > MIN_EVENT_GAP:
-                    events.append({
-                        "time": current_time,
-                        "score": p_surprise,
-                        "frame": frame.copy()
-                    })
-                else:
-                    if p_surprise > events[-1]["score"]:
-                        events[-1]["time"] = current_time
-                        events[-1]["score"] = p_surprise
-                        events[-1]["frame"] = frame.copy()
-
-            label = f"😲 SURPRISE (p={p_surprise:.2f})"
-            color = (0, 255, 0)
-
-        else:
-            label = f"🙂 Not Surprise (p={p_surprise:.2f})"
-            color = (0, 0, 255)
-
-        # Draw bounding box
-        cv2.rectangle(frame_bgr, (x, y), (x + w, y + h), color, 3)
-
-    # -------- Label 위치: 왼쪽 아래 + 큰 글씨 --------
-    h_img, w_img = frame_bgr.shape[:2]
-    cv2.putText(
-        frame_bgr,
-        label,
-        (10, h_img - 10),
-        cv2.FONT_HERSHEY_SIMPLEX,
-        1.6,
-        color,
-        3
-    )
-
-    out_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
-
-    # Per-frame bar chart
-    fig = go.Figure()
-    if len(probs_dict) > 0:
-        fig.add_trace(go.Bar(
-            x=list(probs_dict.keys()),
-            y=list(probs_dict.values()),
-            marker_color="lightskyblue"
-        ))
-        fig.update_layout(
-            title="Emotion Probability Distribution",
-            yaxis=dict(range=[0, 1])
-        )
-
-    session_duration_str = format_time(current_time)
-    stats_text = (
-        "### Session Stats\n"
-        f"- Session duration: {session_duration_str}\n"
-        f"- Current threshold: {threshold:.2f}\n"
-        f"- Frames with face detected: {frames_with_face}\n"
-        f"- Surprise events detected: {len(events)}\n"
-        f"- Max P(surprise): {max_p_surprise:.2f}\n"
-    )
-
-    return out_rgb, probs_dict, fig, stats_text
-
-
-# ===============================
-# 6. PDF Generation
-# ===============================
-def create_pdf(summary_text, top_images, timeline_fig):
-    os.makedirs("reports", exist_ok=True)
-    timestamp = int(time.time())
-    pdf_path = os.path.join("reports", f"surprise_report_{timestamp}.pdf")
-
-    timeline_path = os.path.join("reports", f"timeline_{timestamp}.png")
-    timeline_fig.savefig(timeline_path, bbox_inches="tight")
-
-    img_paths = []
-    for i, img in enumerate(top_images):
-        if img is None:
-            img_paths.append(None)
-            continue
-        img_pil = Image.fromarray(img)
-        img_path = os.path.join("reports", f"top{i+1}_{timestamp}.png")
-        img_pil.save(img_path)
-        img_paths.append(img_path)
-
-    pdf = FPDF()
-    pdf.add_page()
-
-    pdf.set_font("Arial", "B", 16)
-    pdf.cell(0, 10, "Real-Time Surprise Detector Report", ln=1)
-
-    pdf.set_font("Arial", "", 11)
-    pdf.multi_cell(0, 6, summary_text)
-    pdf.ln(4)
-
-    pdf.set_font("Arial", "B", 12)
-    pdf.cell(0, 8, "Surprise Probability Timeline", ln=1)
-    pdf.image(timeline_path, w=170)
-    pdf.ln(4)
-
-    pdf.set_font("Arial", "B", 12)
-    pdf.cell(0, 8, "Top Surprise Frames", ln=1)
-    pdf.set_font("Arial", "", 11)
-
-    for i, path in enumerate(img_paths):
-        if path is not None:
-            pdf.cell(0, 6, f"Top {i+1}", ln=1)
-            pdf.image(path, w=80)
-            pdf.ln(2)
-
-    pdf.output(pdf_path)
-    return pdf_path
-
-
-# ===============================
-# 7. Summarize Results
-# ===============================
-def summarize_results():
-
-    global events, start_time, surprise_history
-    global frames_with_face, max_p_surprise
-
-    if len(surprise_history) == 0:
-        return "No data recorded.", None, None, None, None, None
-
-    times = [h["time"] for h in surprise_history]
-    scores = [h["score"] for h in surprise_history]
-
-    fig, ax = plt.subplots()
-    ax.plot(times, scores, marker="o", linewidth=1)
-    ax.set_title("Surprise Probability Timeline")
-    ax.set_xlabel("Time (s)")
-    ax.set_ylabel("P(surprise)")
-    ax.set_ylim(0, 1)
-    ax.grid(True)
-
-    top_images = [None, None, None]
-    if len(events) == 0:
-        summary_text = (
-            "No surprise events detected above the current threshold.\n\n"
-            "The timeline shows overall surprise probability over time."
-        )
-        img1 = img2 = img3 = None
-
-    else:
-        top3 = sorted(events, key=lambda x: x["score"], reverse=True)[:3]
-
-        captions = []
-        images = []
-        top_times = []
-        top_scores = []
-
-        for i, e in enumerate(top3):
-            formatted_time = format_time(e["time"])
-            score = e["score"]
-            captions.append(f"#{i+1}   Time = {formatted_time}   Score = {score:.2f}")
-            images.append(e["frame"])
-            top_times.append(e["time"])
-            top_scores.append(score)
-
-        summary_text = "Top 3 surprise moments:\n" + "\n".join(captions)
-
-        markers = ["*", "^", "s"]
-        colors = ["red", "darkorange", "gold"]
-
-        for i, (t, s) in enumerate(zip(top_times, top_scores)):
-            ax.scatter(t, s, color=colors[i], marker=markers[i], s=80, zorder=5)
-
-        for i in range(3):
-            if i < len(images):
-                top_images[i] = images[i]
-
-        img1, img2, img3 = top_images
-
-    pdf_path = create_pdf(summary_text, top_images, fig)
-
-    events = []
-    start_time = None
-    surprise_history = []
-    frames_with_face = 0
-    max_p_surprise = 0.0
-
-    return summary_text, img1, img2, img3, fig, pdf_path
-
-
-# ===============================
-# 8. UI
-# ===============================
-try:
-    custom_theme = gr.themes.Soft(primary_hue="indigo", neutral_hue="slate")
-except:
-    custom_theme = "soft"
-
-demo = gr.Blocks(theme=custom_theme)
-
-with demo:
-
-    gr.Markdown(
-        """
-        # 🎭 Real-Time Surprise Detector
-        ### A real-time facial reaction analysis system
-        ##### Detects surprise reactions using facial emotion recognition and summarizes top 3 peak surprise moments.
-
-        **How to use:**
-        1. Enable your webcam by clicking the feed area.  
-        2. Watch your chosen video while keeping your face visible.  
-        3. If many frames show **"NO FACE"**, try brighter lighting or adjust your face angle.  
-        4. Click **“Show Top 3 Surprise Moments”** after stopping the stream.  
-        5. Download the generated PDF if needed.
-        ---
-        """
-    )
-
-    with gr.Row():
-        with gr.Column(scale=2):
-
-            webcam = gr.Image(
-                sources=["webcam"],
-                type="numpy",
-                label="Webcam Feed"
-            )
-            output_img = gr.Image(label="Detection Result")
-
-        with gr.Column(scale=1):
-            threshold = gr.Slider(
-                minimum=0.0, maximum=1.0, value=0.1,
-                step=0.01, label="Surprise Threshold"
-            )
-
-            gr.Markdown(
-                """
-                ### What is the Surprise Threshold?
-
-                - Lower threshold → detects smaller reactions  
-                - Higher threshold → detects only strong surprise  
-                - **Default = 0.1**  
-
-                👉 Try making a surprised face to adjust sensitivity.
-                """
-            )
-
-            output_label = gr.Label(label="Softmax Probabilities")
-            plot = gr.Plot(label="Emotion Probability (per frame)")
-            stats_md = gr.Markdown("### Session Stats\nWaiting for stream...")
-
-    webcam.stream(
-        fn=detect_surprise,
-        inputs=[webcam, threshold],
-        outputs=[output_img, output_label, plot, stats_md],
-        stream_every=0.1
-    )
-
-    gr.Markdown("---")
-    gr.Markdown("## 🔍 Summary & Report")
-
-    summarize_button = gr.Button("🎯 Show Top 3 Surprise Moments")
-
-    summary_text = gr.Textbox(
-        label="Top 3 Summary",
-        lines=6,
-        max_lines=10
-    )
-
-    with gr.Row():
-        img1 = gr.Image(label="Top 1")
-        img2 = gr.Image(label="Top 2")
-        img3 = gr.Image(label="Top 3")
-
-    timeline_plot = gr.Plot(label="Surprise Timeline")
-    pdf_file = gr.File(label="Download PDF Report")
-
-    summarize_button.click(
-        fn=summarize_results,
-        inputs=[],
-        outputs=[summary_text, img1, img2, img3, timeline_plot, pdf_file]
-    )
-
-if __name__ == "__main__":
-    demo.launch()
-
-::contentReference[oaicite:0]{index=0}
+import os
+import time
+import cv2
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+import plotly.graph_objects as go
+import matplotlib.pyplot as plt
+from fpdf import FPDF
+from PIL import Image
+
+# ===============================
+# 1. Load Model
+# ===============================
+MODEL_PATH = "fer_surprise_softmax.h5"
+model = tf.keras.models.load_model(MODEL_PATH, compile=False)
+
+IMG_SIZE = (96, 96)
+CLASS_NAMES = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
+SURPRISE_IDX = CLASS_NAMES.index("surprise")
+
+# ===============================
+# 2. Face Detector
+# ===============================
+face_cascade = cv2.CascadeClassifier(
+    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+)
+
+# ===============================
+# 3. State Storage
+# ===============================
+events = []           
+surprise_history = [] 
+start_time = None     
+MIN_EVENT_GAP = 1.0
+
+# Session stats
+frames_with_face = 0  
+max_p_surprise = 0.0  
+
+# ===============================
+# 4. Utility: Time Formatting
+# ===============================
+def format_time(seconds: float) -> str:
+    minutes = int(seconds // 60)
+    sec = int(seconds % 60)
+    return f"{minutes:02d}:{sec:02d}"
+
+
+# ===============================
+# 5. Real-time Frame Processing
+# ===============================
+def detect_surprise(frame, threshold):
+
+    global events, start_time, surprise_history
+    global frames_with_face, max_p_surprise
+
+    if frame is None:
+        stats_text = (
+            "### Session Stats\n"
+            "- Session duration: 00:00\n"
+            f"- Current threshold: {threshold:.2f}\n"
+            "- Frames with face detected: 0\n"
+            "- Surprise events detected: 0\n"
+            "- Max P(surprise): 0.00\n"
+        )
+        return None, {"Error": 1.0}, None, stats_text
+
+    if start_time is None:
+        start_time = time.time()
+        surprise_history = []
+        events = []
+        frames_with_face = 0
+        max_p_surprise = 0.0
+
+    current_time = time.time() - start_time
+
+    frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+    gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)
+
+    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+
+    # 변경된 기본 라벨: 얼굴 미검출 시 조명/각도 안내
+    label = "NO FACE - Try brighter lighting or adjust angle"
+    color = (0, 255, 255)
+    probs_dict = {}
+
+    if len(faces) > 0:
+        frames_with_face += 1
+        x, y, w, h = sorted(faces, key=lambda r: r[2] * r[3], reverse=True)[0]
+        roi = frame_bgr[y:y+h, x:x+w]
+
+        rgb = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
+        resized = cv2.resize(rgb, IMG_SIZE)
+        inp = resized.astype("float32") / 255.0
+        inp = np.expand_dims(inp, axis=0)
+
+        probs = model.predict(inp, verbose=0)[0]
+        p_surprise = float(probs[SURPRISE_IDX])
+
+        if p_surprise > max_p_surprise:
+            max_p_surprise = p_surprise
+
+        probs_dict = {
+            cls: float(p) for cls, p in zip(CLASS_NAMES, probs)
+        }
+
+        surprise_history.append({
+            "time": current_time,
+            "score": p_surprise,
+        })
+
+        # -------- Top3 detection logic --------
+        if p_surprise >= threshold:
+            if len(events) == 0:
+                events.append({
+                    "time": current_time,
+                    "score": p_surprise,
+                    "frame": frame.copy()
+                })
+            else:
+                dt = current_time - events[-1]["time"]
+                if dt > MIN_EVENT_GAP:
+                    events.append({
+                        "time": current_time,
+                        "score": p_surprise,
+                        "frame": frame.copy()
+                    })
+                else:
+                    if p_surprise > events[-1]["score"]:
+                        events[-1]["time"] = current_time
+                        events[-1]["score"] = p_surprise
+                        events[-1]["frame"] = frame.copy()
+
+            label = f"😲 SURPRISE (p={p_surprise:.2f})"
+            color = (0, 255, 0)
+
+        else:
+            label = f"🙂 Not Surprise (p={p_surprise:.2f})"
+            color = (0, 0, 255)
+
+        # Draw bounding box
+        cv2.rectangle(frame_bgr, (x, y), (x + w, y + h), color, 3)
+
+    # -------- Label 위치: 왼쪽 아래 + 큰 글씨 --------
+    h_img, w_img = frame_bgr.shape[:2]
+    cv2.putText(
+        frame_bgr,
+        label,
+        (10, h_img - 10),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        1.6,
+        color,
+        3
+    )
+
+    out_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
+
+    # Per-frame bar chart
+    fig = go.Figure()
+    if len(probs_dict) > 0:
+        fig.add_trace(go.Bar(
+            x=list(probs_dict.keys()),
+            y=list(probs_dict.values()),
+            marker_color="lightskyblue"
+        ))
+        fig.update_layout(
+            title="Emotion Probability Distribution",
+            yaxis=dict(range=[0, 1])
+        )
+
+    session_duration_str = format_time(current_time)
+    stats_text = (
+        "### Session Stats\n"
+        f"- Session duration: {session_duration_str}\n"
+        f"- Current threshold: {threshold:.2f}\n"
+        f"- Frames with face detected: {frames_with_face}\n"
+        f"- Surprise events detected: {len(events)}\n"
+        f"- Max P(surprise): {max_p_surprise:.2f}\n"
+    )
+
+    return out_rgb, probs_dict, fig, stats_text
+
+
+# ===============================
+# 6. PDF Generation
+# ===============================
+def create_pdf(summary_text, top_images, timeline_fig):
+    os.makedirs("reports", exist_ok=True)
+    timestamp = int(time.time())
+    pdf_path = os.path.join("reports", f"surprise_report_{timestamp}.pdf")
+
+    timeline_path = os.path.join("reports", f"timeline_{timestamp}.png")
+    timeline_fig.savefig(timeline_path, bbox_inches="tight")
+
+    img_paths = []
+    for i, img in enumerate(top_images):
+        if img is None:
+            img_paths.append(None)
+            continue
+        img_pil = Image.fromarray(img)
+        img_path = os.path.join("reports", f"top{i+1}_{timestamp}.png")
+        img_pil.save(img_path)
+        img_paths.append(img_path)
+
+    pdf = FPDF()
+    pdf.add_page()
+
+    pdf.set_font("Arial", "B", 16)
+    pdf.cell(0, 10, "Real-Time Surprise Detector Report", ln=1)
+
+    pdf.set_font("Arial", "", 11)
+    pdf.multi_cell(0, 6, summary_text)
+    pdf.ln(4)
+
+    pdf.set_font("Arial", "B", 12)
+    pdf.cell(0, 8, "Surprise Probability Timeline", ln=1)
+    pdf.image(timeline_path, w=170)
+    pdf.ln(4)
+
+    pdf.set_font("Arial", "B", 12)
+    pdf.cell(0, 8, "Top Surprise Frames", ln=1)
+    pdf.set_font("Arial", "", 11)
+
+    for i, path in enumerate(img_paths):
+        if path is not None:
+            pdf.cell(0, 6, f"Top {i+1}", ln=1)
+            pdf.image(path, w=80)
+            pdf.ln(2)
+
+    pdf.output(pdf_path)
+    return pdf_path
+
+
+# ===============================
+# 7. Summarize Results
+# ===============================
+def summarize_results():
+
+    global events, start_time, surprise_history
+    global frames_with_face, max_p_surprise
+
+    if len(surprise_history) == 0:
+        return "No data recorded.", None, None, None, None, None
+
+    times = [h["time"] for h in surprise_history]
+    scores = [h["score"] for h in surprise_history]
+
+    fig, ax = plt.subplots()
+    ax.plot(times, scores, marker="o", linewidth=1)
+    ax.set_title("Surprise Probability Timeline")
+    ax.set_xlabel("Time (s)")
+    ax.set_ylabel("P(surprise)")
+    ax.set_ylim(0, 1)
+    ax.grid(True)
+
+    top_images = [None, None, None]
+    if len(events) == 0:
+        summary_text = (
+            "No surprise events detected above the current threshold.\n\n"
+            "The timeline shows overall surprise probability over time."
+        )
+        img1 = img2 = img3 = None
+
+    else:
+        top3 = sorted(events, key=lambda x: x["score"], reverse=True)[:3]
+
+        captions = []
+        images = []
+        top_times = []
+        top_scores = []
+
+        for i, e in enumerate(top3):
+            formatted_time = format_time(e["time"])
+            score = e["score"]
+            captions.append(f"#{i+1}   Time = {formatted_time}   Score = {score:.2f}")
+            images.append(e["frame"])
+            top_times.append(e["time"])
+            top_scores.append(score)
+
+        summary_text = "Top 3 surprise moments:\n" + "\n".join(captions)
+
+        markers = ["*", "^", "s"]
+        colors = ["red", "darkorange", "gold"]
+
+        for i, (t, s) in enumerate(zip(top_times, top_scores)):
+            ax.scatter(t, s, color=colors[i], marker=markers[i], s=80, zorder=5)
+
+        for i in range(3):
+            if i < len(images):
+                top_images[i] = images[i]
+
+        img1, img2, img3 = top_images
+
+    pdf_path = create_pdf(summary_text, top_images, fig)
+
+    events = []
+    start_time = None
+    surprise_history = []
+    frames_with_face = 0
+    max_p_surprise = 0.0
+
+    return summary_text, img1, img2, img3, fig, pdf_path
+
+
+# ===============================
+# 8. UI
+# ===============================
+try:
+    custom_theme = gr.themes.Soft(primary_hue="indigo", neutral_hue="slate")
+except:
+    custom_theme = "soft"
+
+demo = gr.Blocks(theme=custom_theme)
+
+with demo:
+
+    gr.Markdown(
+        """
+        # 🎭 Real-Time Surprise Detector
+        ### A real-time facial reaction analysis system
+        ##### Detects surprise reactions using facial emotion recognition and summarizes top 3 peak surprise moments.
+
+        **How to use:**
+        1. Enable your webcam by clicking the feed area.  
+        2. Watch your chosen video while keeping your face visible.  
+        3. If many frames show **"NO FACE"**, try brighter lighting or adjust your face angle.  
+        4. Click **“Show Top 3 Surprise Moments”** after stopping the stream.  
+        5. Download the generated PDF if needed.
+        ---
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=2):
+
+            webcam = gr.Image(
+                sources=["webcam"],
+                type="numpy",
+                label="Webcam Feed"
+            )
+            output_img = gr.Image(label="Detection Result")
+
+        with gr.Column(scale=1):
+            threshold = gr.Slider(
+                minimum=0.0, maximum=1.0, value=0.1,
+                step=0.01, label="Surprise Threshold"
+            )
+
+            gr.Markdown(
+                """
+                ### What is the Surprise Threshold?
+
+                - Lower threshold → detects smaller reactions  
+                - Higher threshold → detects only strong surprise  
+                - **Default = 0.1**  
+
+                👉 Try making a surprised face to adjust sensitivity.
+                """
+            )
+
+            output_label = gr.Label(label="Softmax Probabilities")
+            plot = gr.Plot(label="Emotion Probability (per frame)")
+            stats_md = gr.Markdown("### Session Stats\nWaiting for stream...")
+
+    webcam.stream(
+        fn=detect_surprise,
+        inputs=[webcam, threshold],
+        outputs=[output_img, output_label, plot, stats_md],
+        stream_every=0.1
+    )
+
+    gr.Markdown("---")
+    gr.Markdown("## 🔍 Summary & Report")
+
+    summarize_button = gr.Button("🎯 Show Top 3 Surprise Moments")
+
+    summary_text = gr.Textbox(
+        label="Top 3 Summary",
+        lines=6,
+        max_lines=10
+    )
+
+    with gr.Row():
+        img1 = gr.Image(label="Top 1")
+        img2 = gr.Image(label="Top 2")
+        img3 = gr.Image(label="Top 3")
+
+    timeline_plot = gr.Plot(label="Surprise Timeline")
+    pdf_file = gr.File(label="Download PDF Report")
+
+    summarize_button.click(
+        fn=summarize_results,
+        inputs=[],
+        outputs=[summary_text, img1, img2, img3, timeline_plot, pdf_file]
+    )
+
+if __name__ == "__main__":
+    demo.launch()