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| import streamlit as st | |
| import numpy as np | |
| import cv2 | |
| import os | |
| import random | |
| import tempfile | |
| import joblib | |
| from typing import Optional, Tuple, Dict, Any, List | |
| # NEW: use components.html for the long HTML report | |
| import streamlit.components.v1 as components | |
| # (Optional) Face landmarks similar to earlier app | |
| try: | |
| import mediapipe as mp | |
| mp_face_mesh = mp.solutions.face_mesh | |
| face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, | |
| refine_landmarks=True, min_detection_confidence=0.5) | |
| except Exception: | |
| mp = None | |
| face_mesh = None | |
| # Live video capture (front-end) with WebRTC | |
| from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration | |
| import av | |
| from collections import deque | |
| st.set_page_config(page_title="Face-based Lab Test AI Report", layout="wide") | |
| # ============================== | |
| # WebRTC config | |
| # ============================== | |
| RTC_CONFIGURATION = RTCConfiguration( | |
| {"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]} | |
| ) | |
| # ============================== | |
| # Utility & UI helpers (copied style) | |
| # ============================== | |
| def get_risk_color(value: float, normal_range: Tuple[float, float]) -> Tuple[str, str, str]: | |
| low, high = normal_range | |
| if value < low: | |
| return ("Low", "🔻", "#FFCCCC") | |
| elif value > high: | |
| return ("High", "🔺", "#FFE680") | |
| else: | |
| return ("Normal", "✅", "#CCFFCC") | |
| def build_table(title: str, rows: List[Tuple[str, float, Tuple[float, float]]]) -> str: | |
| html = ( | |
| f'<div style="margin-bottom: 24px;">' | |
| f'<h4 style="margin: 8px 0;">{title}</h4>' | |
| f'<table style="width:100%; border-collapse:collapse;">' | |
| f'<thead><tr style="background:#f0f0f0;">' | |
| f'<th style="padding:8px;border:1px solid #ccc;">Test</th>' | |
| f'<th style="padding:8px;border:1px solid #ccc;">Result</th>' | |
| f'<th style="padding:8px;border:1px solid #ccc;">Expected Range</th>' | |
| f'<th style="padding:8px;border:1px solid #ccc;">Level</th>' | |
| f'</tr></thead><tbody>' | |
| ) | |
| for label, value, ref in rows: | |
| level, icon, bg = get_risk_color(value, ref) | |
| html += ( | |
| f'<tr style="background:{bg};">' | |
| f'<td style="padding:6px;border:1px solid #ccc;">{label}</td>' | |
| f'<td style="padding:6px;border:1px solid #ccc;">{value:.2f}</td>' | |
| f'<td style="padding:6px;border:1px solid #ccc;">{ref[0]} – {ref[1]}</td>' | |
| f'<td style="padding:6px;border:1px solid #ccc;">{icon} {level}</td>' | |
| f'</tr>' | |
| ) | |
| html += '</tbody></table></div>' | |
| return html | |
| def extract_basic_rgb_features(image_rgb: np.ndarray) -> List[float]: | |
| red_percent = 100 * float(np.mean(image_rgb[:, :, 0])) / 255.0 | |
| green_percent = 100 * float(np.mean(image_rgb[:, :, 1])) / 255.0 | |
| blue_percent = 100 * float(np.mean(image_rgb[:, :, 2])) / 255.0 | |
| return [red_percent, green_percent, blue_percent] | |
| def blur_for_display(img_rgb: np.ndarray, k: int = 35) -> np.ndarray: | |
| k = max(3, k | 1) # odd kernel | |
| return cv2.GaussianBlur(img_rgb, (k, k), 0) | |
| # ============================== | |
| # Model loading (guarded) | |
| # ============================== | |
| def safe_load_joblib(path: str): | |
| try: | |
| if os.path.exists(path): | |
| return joblib.load(path) | |
| except Exception: | |
| pass | |
| return None | |
| hemoglobin_model = safe_load_joblib("hemoglobin_model_from_anemia_dataset.pkl") | |
| hr_model = safe_load_joblib("heart_rate_model.pkl") | |
| spo2_model = safe_load_joblib("spo2_model_simulated.pkl") | |
| hemoglobin_r2 = 0.385 # as shown in your previous UI | |
| # ============================== | |
| # Placeholder models | |
| # ============================== | |
| def random_model_predict(low: float, high: float) -> float: | |
| return random.uniform(low, high) | |
| # ============================== | |
| # Image path (analyze image once, show only final blurred frame) | |
| # ============================== | |
| def analyze_face(image_bgr: Optional[np.ndarray]) -> Tuple[str, Optional[np.ndarray]]: | |
| if image_bgr is None: | |
| return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None | |
| frame_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) | |
| if face_mesh is not None and mp is not None: | |
| result = face_mesh.process(frame_rgb) | |
| if not result.multi_face_landmarks: | |
| return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None | |
| features = extract_basic_rgb_features(frame_rgb) | |
| if hemoglobin_model is not None: | |
| try: | |
| hb_pred = float(hemoglobin_model.predict([features])[0]) | |
| except Exception: | |
| hb_pred = float(random_model_predict(12.5, 15.0)) | |
| else: | |
| hb_pred = float(random_model_predict(12.5, 15.0)) | |
| heart_rate = float(random_model_predict(60, 100)) | |
| spo2 = float(random_model_predict(95, 100)) | |
| rr = int(12 + abs(int(heart_rate) % 5 - 2)) | |
| test_values: Dict[str, Any] = { | |
| "Hemoglobin": hb_pred, | |
| "WBC Count": random_model_predict(4.0, 11.0), | |
| "Platelet Count": random_model_predict(150, 450), | |
| "Iron": random_model_predict(60, 170), | |
| "Ferritin": random_model_predict(30, 300), | |
| "TIBC": random_model_predict(250, 400), | |
| "Bilirubin": random_model_predict(0.3, 1.2), | |
| "Creatinine": random_model_predict(0.6, 1.2), | |
| "Urea": random_model_predict(7, 20), | |
| "Sodium": random_model_predict(135, 145), | |
| "Potassium": random_model_predict(3.5, 5.1), | |
| "TSH": random_model_predict(0.4, 4.0), | |
| "Cortisol": random_model_predict(5, 25), | |
| "FBS": random_model_predict(70, 110), | |
| "HbA1c": random_model_predict(4.0, 5.7), | |
| "Albumin": random_model_predict(3.5, 5.5), | |
| "BP Systolic": random_model_predict(90, 120), | |
| "BP Diastolic": random_model_predict(60, 80), | |
| "Temperature": random_model_predict(97, 99), | |
| } | |
| html_output = "".join([ | |
| f'<div style="font-size:14px;color:#888;margin-bottom:10px;">Hemoglobin R² Score: {hemoglobin_r2:.2f}</div>', | |
| build_table("🩸 Hematology", [ | |
| ("Hemoglobin", test_values["Hemoglobin"], (13.5, 17.5)), | |
| ("WBC Count", test_values["WBC Count"], (4.0, 11.0)), | |
| ("Platelet Count", test_values["Platelet Count"], (150, 450)), | |
| ]), | |
| build_table("🧬 Iron Panel", [ | |
| ("Iron", test_values["Iron"], (60, 170)), | |
| ("Ferritin", test_values["Ferritin"], (30, 300)), | |
| ("TIBC", test_values["TIBC"], (250, 400)), | |
| ]), | |
| build_table("🧬 Liver & Kidney", [ | |
| ("Bilirubin", test_values["Bilirubin"], (0.3, 1.2)), | |
| ("Creatinine", test_values["Creatinine"], (0.6, 1.2)), | |
| ("Urea", test_values["Urea"], (7, 20)), | |
| ]), | |
| build_table("🧪 Electrolytes", [ | |
| ("Sodium", test_values["Sodium"], (135, 145)), | |
| ("Potassium", test_values["Potassium"], (3.5, 5.1)), | |
| ]), | |
| build_table("🧁 Metabolic & Thyroid", [ | |
| ("FBS", test_values["FBS"], (70, 110)), | |
| ("HbA1c", test_values["HbA1c"], (4.0, 5.7)), | |
| ("TSH", test_values["TSH"], (0.4, 4.0)), | |
| ]), | |
| build_table("❤️ Vitals", [ | |
| ("SpO2", spo2, (95, 100)), | |
| ("Heart Rate", heart_rate, (60, 100)), | |
| ("Respiratory Rate", rr, (12, 20)), | |
| ("Temperature", test_values["Temperature"], (97, 99)), | |
| ("BP Systolic", test_values["BP Systolic"], (90, 120)), | |
| ("BP Diastolic", test_values["BP Diastolic"], (60, 80)), | |
| ]), | |
| build_table("🩹 Other Indicators", [ | |
| ("Cortisol", test_values["Cortisol"], (5, 25)), | |
| ("Albumin", test_values["Albumin"], (3.5, 5.5)), | |
| ]) | |
| ]) | |
| summary = "<div style='margin-top:20px;padding:12px;border:1px dashed #999;background:#fcfcfc;'>" | |
| summary += "<h4>📝 Summary for You</h4><ul>" | |
| if hb_pred < 13.5: | |
| summary += "<li>Your hemoglobin is a bit low — this could mean mild anemia.</li>" | |
| if test_values["Iron"] < 60 or test_values["Ferritin"] < 30: | |
| summary += "<li>Low iron storage detected — consider an iron profile test.</li>" | |
| if test_values["Bilirubin"] > 1.2: | |
| summary += "<li>Elevated bilirubin — possible jaundice. Recommend LFT.</li>" | |
| if test_values["HbA1c"] > 5.7: | |
| summary += "<li>High HbA1c — prediabetes indication. Recommend glucose check.</li>" | |
| if spo2 < 95: | |
| summary += "<li>Low SpO₂ — suggest retesting with a pulse oximeter.</li>" | |
| summary += "</ul><p><strong>💡 Tip:</strong> This is an AI-based estimate. Please follow up with a lab.</p></div>" | |
| html_output += summary | |
| html_output += "<br><div style='margin-top:20px;padding:12px;border:2px solid #2d87f0;background:#f2faff;text-align:center;border-radius:8px;'>" | |
| html_output += "<h4>📞 Book a Lab Test</h4><p>Prefer confirmation? Find certified labs near you.</p>" | |
| html_output += "<button style='padding:10px 20px;background:#007BFF;color:#fff;border:none;border-radius:5px;cursor:pointer;'>Find Labs Near Me</button></div>" | |
| # Return the single final frame (blurred for display only) | |
| return html_output, blur_for_display(frame_rgb) | |
| # ============================== | |
| # Video path (assemble once; show only final blurred keyframe) | |
| # ============================== | |
| def analyze_video(video_path: Optional[str]) -> Tuple[str, Optional[np.ndarray]]: | |
| if not video_path or not os.path.exists(video_path): | |
| return "<div style='color:red;'>⚠️ Face video missing or unreadable.</div>", None | |
| cap = cv2.VideoCapture(video_path) | |
| frame_sample = None | |
| frames = 0 | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| if frame_sample is None: | |
| frame_sample = frame.copy() | |
| frames += 1 | |
| cap.release() | |
| if frame_sample is None or frames < 30: | |
| return "<div style='color:red;'>⚠️ Video too short; please record ~20–30s.</div>", None | |
| # Placeholder HR/SpO2 (replace with proper rPPG) | |
| try: | |
| hr_value = float(random_model_predict(65, 85)) | |
| gray = cv2.cvtColor(frame_sample, cv2.COLOR_BGR2GRAY) | |
| brightness_var = float(np.std(gray) / 255.0) | |
| skin_tone_index = float(np.mean(frame_sample[100:150, 100:150]) / 255.0) if frame_sample[100:150, 100:150].size else 0.5 | |
| spo2 = float(98.0 - (1.5 * brightness_var - 0.5 * (skin_tone_index - 0.5))) | |
| spo2 = float(np.clip(spo2, 92.0, 100.0)) | |
| except Exception: | |
| hr_value = float(random_model_predict(65, 85)) | |
| spo2 = float(random_model_predict(95, 100)) | |
| rr = int(12 + abs(int(hr_value) % 5 - 2)) | |
| test_values: Dict[str, Any] = { | |
| "Hemoglobin": random_model_predict(12.5, 15.0), | |
| "WBC Count": random_model_predict(4.0, 11.0), | |
| "Platelet Count": random_model_predict(150, 450), | |
| "Iron": random_model_predict(60, 170), | |
| "Ferritin": random_model_predict(30, 300), | |
| "TIBC": random_model_predict(250, 400), | |
| "Bilirubin": random_model_predict(0.3, 1.2), | |
| "Creatinine": random_model_predict(0.6, 1.2), | |
| "Urea": random_model_predict(7, 20), | |
| "Sodium": random_model_predict(135, 145), | |
| "Potassium": random_model_predict(3.5, 5.1), | |
| "TSH": random_model_predict(0.4, 4.0), | |
| "Cortisol": random_model_predict(5, 25), | |
| "FBS": random_model_predict(70, 110), | |
| "HbA1c": random_model_predict(4.0, 5.7), | |
| "Albumin": random_model_predict(3.5, 5.5), | |
| "BP Systolic": random_model_predict(90, 120), | |
| "BP Diastolic": random_model_predict(60, 80), | |
| "Temperature": random_model_predict(97, 99), | |
| } | |
| frame_rgb = cv2.cvtColor(frame_sample, cv2.COLOR_BGR2RGB) | |
| html_output = "".join([ | |
| f'<div style="font-size:14px;color:#888;margin-bottom:10px;">Hemoglobin R² Score: {hemoglobin_r2:.2f}</div>', | |
| build_table("🩸 Hematology", [ | |
| ("Hemoglobin", test_values["Hemoglobin"], (13.5, 17.5)), | |
| ("WBC Count", test_values["WBC Count"], (4.0, 11.0)), | |
| ("Platelet Count", test_values["Platelet Count"], (150, 450)), | |
| ]), | |
| build_table("🧬 Iron Panel", [ | |
| ("Iron", test_values["Iron"], (60, 170)), | |
| ("Ferritin", test_values["Ferritin"], (30, 300)), | |
| ("TIBC", test_values["TIBC"], (250, 400)), | |
| ]), | |
| build_table("🧬 Liver & Kidney", [ | |
| ("Bilirubin", test_values["Bilirubin"], (0.3, 1.2)), | |
| ("Creatinine", test_values["Creatinine"], (0.6, 1.2)), | |
| ("Urea", test_values["Urea"], (7, 20)), | |
| ]), | |
| build_table("🧪 Electrolytes", [ | |
| ("Sodium", test_values["Sodium"], (135, 145)), | |
| ("Potassium", test_values["Potassium"], (3.5, 5.1)), | |
| ]), | |
| build_table("🧁 Metabolic & Thyroid", [ | |
| ("FBS", test_values["FBS"], (70, 110)), | |
| ("HbA1c", test_values["HbA1c"], (4.0, 5.7)), | |
| ("TSH", test_values["TSH"], (0.4, 4.0)), | |
| ]), | |
| build_table("❤️ Vitals", [ | |
| ("SpO2", spo2, (95, 100)), | |
| ("Heart Rate", hr_value, (60, 100)), | |
| ("Respiratory Rate", rr, (12, 20)), | |
| ("Temperature", test_values["Temperature"], (97, 99)), | |
| ("BP Systolic", test_values["BP Systolic"], (90, 120)), | |
| ("BP Diastolic", test_values["BP Diastolic"], (60, 80)), | |
| ]), | |
| build_table("🩹 Other Indicators", [ | |
| ("Cortisol", test_values["Cortisol"], (5, 25)), | |
| ("Albumin", test_values["Albumin"], (3.5, 5.5)), | |
| ]) | |
| ]) | |
| summary = "<div style='margin-top:20px;padding:12px;border:1px dashed #999;background:#fcfcfc;'>" | |
| summary += "<h4>📝 Summary for You</h4><ul>" | |
| if test_values["Hemoglobin"] < 13.5: | |
| summary += "<li>Your hemoglobin is a bit low — this could mean mild anemia.</li>" | |
| if test_values["Iron"] < 60 or test_values["Ferritin"] < 30: | |
| summary += "<li>Low iron storage detected — consider an iron profile test.</li>" | |
| if test_values["Bilirubin"] > 1.2: | |
| summary += "<li>Elevated bilirubin — possible jaundice. Recommend LFT.</li>" | |
| if test_values["HbA1c"] > 5.7: | |
| summary += "<li>High HbA1c — prediabetes indication. Recommend glucose check.</li>" | |
| if spo2 < 95: | |
| summary += "<li>Low SpO₂ — suggest retesting with a pulse oximeter.</li>" | |
| summary += "</ul><p><strong>💡 Tip:</strong> This is an AI-based estimate. Please follow up with a lab.</p></div>" | |
| html_output += summary | |
| html_output += "<br><div style='margin-top:20px;padding:12px;border:2px solid #2d87f0;background:#f2faff;text-align:center;border-radius:8px;'>" | |
| html_output += "<h4>📞 Book a Lab Test</h4><p>Prefer confirmation? Find certified labs near you.</p>" | |
| html_output += "<button style='padding:10px 20px;background:#007BFF;color:#fff;border:none;border-radius:5px;cursor:pointer;'>Find Labs Near Me</button></div>" | |
| return html_output, blur_for_display(frame_rgb) | |
| # ============================== | |
| # WebRTC frame collector (no progress timer to avoid constant re-renders) | |
| # ============================== | |
| class HRCollectorVideoProcessor: | |
| def __init__(self): | |
| self.frames = deque(maxlen=30 * 90) # up to ~90s @30fps | |
| self.recording = False | |
| def recv(self, frame: av.VideoFrame) -> av.VideoFrame: | |
| img = frame.to_ndarray(format="bgr24") | |
| if self.recording: | |
| self.frames.append(img) | |
| return av.VideoFrame.from_ndarray(img, format="bgr24") | |
| def start(self): | |
| self.recording = True | |
| self.frames.clear() | |
| def stop_and_dump_to_file(self) -> Optional[str]: | |
| self.recording = False | |
| if len(self.frames) < 30: # ~1s | |
| return None | |
| h, w = self.frames[0].shape[:2] | |
| fourcc = cv2.VideoWriter_fourcc(*"mp4v") | |
| tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") | |
| tmp_path = tmp.name | |
| tmp.close() | |
| out = cv2.VideoWriter(tmp_path, fourcc, 30.0, (w, h)) | |
| for f in self.frames: | |
| out.write(f) | |
| out.release() | |
| return tmp_path | |
| # ============================== | |
| # Streamlit UI | |
| # ============================== | |
| st.markdown(""" | |
| # 🧠 Face-Based Lab Test AI Report | |
| Use **Image** (Hb) or **Video** (HR). Only a single **blurred preview** is shown **after** analysis to avoid flicker. | |
| """) | |
| # Session state to hold captured assets between form submits | |
| if "captured_image_bgr" not in st.session_state: | |
| st.session_state.captured_image_bgr = None | |
| if "captured_video_path" not in st.session_state: | |
| st.session_state.captured_video_path = None | |
| # NEW: buffer raw camera bytes in case user forgets to click "Save Image" | |
| if "img_bytes_buffer" not in st.session_state: | |
| st.session_state.img_bytes_buffer = None | |
| mode = st.radio("Choose Input Mode", ["Image", "Video"], horizontal=True) | |
| col_left, col_right = st.columns([1.2, 1], gap="large") | |
| with col_left: | |
| if mode == "Image": | |
| st.subheader("📸 Face / Eye Image") | |
| # Wrap inputs in a form so widget changes don't re-run the app | |
| with st.form("img_form", clear_on_submit=False): | |
| img_source = st.radio("Source", ["Camera", "Upload"], horizontal=True, key="img_source") | |
| uploaded_img = None | |
| if img_source == "Camera": | |
| # KEY ADDED + auto-buffer captured bytes | |
| cam_img = st.camera_input("Capture image", key="camera_img") | |
| if cam_img is not None: | |
| st.session_state.img_bytes_buffer = cam_img.getvalue() | |
| else: | |
| file_up = st.file_uploader("Upload JPG/PNG", type=["jpg", "jpeg", "png"]) | |
| if file_up is not None: | |
| uploaded_img = file_up.read() | |
| submitted_img = st.form_submit_button("Save Image") | |
| if submitted_img: | |
| # Prefer explicit upload; else fall back to auto-buffered camera bytes | |
| raw_bytes = uploaded_img if uploaded_img else st.session_state.img_bytes_buffer | |
| if raw_bytes: | |
| arr = np.frombuffer(raw_bytes, dtype=np.uint8) | |
| img_bgr = cv2.imdecode(arr, cv2.IMREAD_COLOR) | |
| if img_bgr is not None: | |
| st.session_state.captured_image_bgr = img_bgr | |
| st.success("Image saved. Now click **Analyze Image** below.") | |
| else: | |
| st.warning("Could not decode the image. Please recapture/upload.") | |
| else: | |
| st.warning("No image provided.") | |
| analyze_image = st.button("🔍 Analyze Image", type="primary", use_container_width=True) | |
| else: | |
| st.subheader("📽 Face Video") | |
| with st.form("vid_form", clear_on_submit=False): | |
| vid_source = st.radio("Source", ["Camera (Live)", "Upload"], horizontal=True, key="vid_source") | |
| temp_video_path = None | |
| if vid_source == "Upload": | |
| up_vid = st.file_uploader("Upload MP4/AVI/MOV", type=["mp4", "avi", "mov"]) | |
| if up_vid is not None: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmpf: | |
| tmpf.write(up_vid.read()) | |
| temp_video_path = tmpf.name | |
| else: | |
| st.write("Start → record ~20–30s → Stop & Use") | |
| if "webrtc_ctx" not in st.session_state: | |
| st.session_state.webrtc_ctx = None | |
| st.session_state.hr_processor = HRCollectorVideoProcessor() | |
| ctx = webrtc_streamer( | |
| key="hr-webrtc", | |
| mode=WebRtcMode.SENDRECV, | |
| rtc_configuration=RTC_CONFIGURATION, | |
| media_stream_constraints={"video": True, "audio": False}, | |
| video_processor_factory=lambda: st.session_state.hr_processor, | |
| ) | |
| c1, c2 = st.columns(2) | |
| with c1: | |
| start_clicked = st.form_submit_button("Start Recording") | |
| with c2: | |
| stop_clicked = st.form_submit_button("Stop & Use") | |
| if start_clicked and ctx.state.playing: | |
| st.session_state.hr_processor.start() | |
| st.info("Recording started...") | |
| if stop_clicked: | |
| dump_path = st.session_state.hr_processor.stop_and_dump_to_file() | |
| if dump_path and os.path.exists(dump_path): | |
| temp_video_path = dump_path | |
| st.success("Video captured. Now click **Analyze Video** below.") | |
| else: | |
| st.warning("Captured video too short. Please record ~20–30 seconds.") | |
| # Store selected/captured video after form submit | |
| submitted_vid = st.form_submit_button("Save Video") | |
| if submitted_vid: | |
| if temp_video_path: | |
| st.session_state.captured_video_path = temp_video_path | |
| elif vid_source == "Upload": | |
| st.warning("No video uploaded.") | |
| else: | |
| st.warning("No video captured yet.") | |
| analyze_video_btn = st.button("🔍 Analyze Video", type="primary", use_container_width=True) | |
| with col_right: | |
| st.subheader("🔒 Blurred Preview (shown only after analysis)") | |
| preview_placeholder = st.empty() | |
| st.markdown("---") | |
| # Single HTML report placeholder | |
| report_placeholder = st.empty() | |
| # Run analysis only when the explicit Analyze buttons are pressed | |
| if mode == "Image" and 'analyze_image' in locals() and analyze_image: | |
| # If user forgot "Save Image", try auto-buffer | |
| if st.session_state.captured_image_bgr is None and st.session_state.img_bytes_buffer is not None: | |
| arr = np.frombuffer(st.session_state.img_bytes_buffer, dtype=np.uint8) | |
| img_bgr = cv2.imdecode(arr, cv2.IMREAD_COLOR) | |
| if img_bgr is not None: | |
| st.session_state.captured_image_bgr = img_bgr | |
| html, frame_rgb_blurred = analyze_face(st.session_state.captured_image_bgr) | |
| if frame_rgb_blurred is not None: | |
| preview_placeholder.image(frame_rgb_blurred, caption="Blurred Image", use_container_width=True) | |
| with report_placeholder: | |
| components.html(html, height=1200, scrolling=True) | |
| elif mode == "Video" and 'analyze_video_btn' in locals() and analyze_video_btn: | |
| html, frame_rgb_blurred = analyze_video(st.session_state.captured_video_path) | |
| if frame_rgb_blurred is not None: | |
| preview_placeholder.image(frame_rgb_blurred, caption="Blurred Key Frame", use_container_width=True) | |
| with report_placeholder: | |
| components.html(html, height=1200, scrolling=True) | |