Create app.py

app.py

@@ -1,243 +1,67 @@
 import streamlit as st
+import cv2
+import torch
+import pandas as pd
+import os
 from PIL import Image
-from ultralytics import YOLO
-import cv2, os
-from datetime import datetime
 import numpy as np
-from dotenv import load_dotenv
-import irm_cancer_module
 
-# ---------------- Charger config ----------------
-load_dotenv()
-SAVE_LIMIT_FREE = int(os.getenv("SAVE_LIMIT_FREE", 5))
-PREMIUM_KEY = os.getenv("PREMIUM_KEY", "VOTRE_CLE_PREMIUM")
-
-# ---------------- Config générale ----------------
-MODEL_PATH = "best.pt"
-MODEL_IRM_PATH = "best_seg.pt"
-MODEL_STROKE_PATH = "stroke.pt"
-SAVE_DIR = os.path.join("/tmp", "results")
-os.makedirs(SAVE_DIR, exist_ok=True)
-
-# ---------------- Charger modèles YOLO ----------------
-model = YOLO(MODEL_PATH)
-model_irm = YOLO(MODEL_IRM_PATH)
-model_stroke = YOLO(MODEL_STROKE_PATH)
-
-# ---------------- Etat utilisateur ----------------
-for key in ["uploads_count", "uploads_count_irm", "uploads_count_stroke", "premium_access"]:
-    if key not in st.session_state:
-        st.session_state[key] = 0 if "count" in key else False
-
-# ---------------- Fonctions utilitaires ----------------
-def _largest_face_bbox(np_img):
-    import mediapipe as mp
-    mp_face_detection = mp.solutions.face_detection
-    h, w = np_img.shape[:2]
-    with mp_face_detection.FaceDetection(min_detection_confidence=0.6) as fd:
-        results = fd.process(cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR))
-        if not results.detections:
-            return None
-        boxes = []
-        for det in results.detections:
-            rel = det.location_data.relative_bounding_box
-            x1 = int(max(0, rel.xmin) * w)
-            y1 = int(max(0, rel.ymin) * h)
-            x2 = int(min(1.0, rel.xmin + rel.width) * w)
-            y2 = int(min(1.0, rel.ymin + rel.height) * h)
-            boxes.append((x1, y1, x2, y2))
-        boxes.sort(key=lambda b: (b[2]-b[0])*(b[3]-b[1]), reverse=True)
-        return boxes[0] if boxes else None
-
-def check_limit(counter_name="uploads_count"):
-    """Vérifie la limite gratuite."""
-    if not st.session_state.premium_access and st.session_state[counter_name] >= SAVE_LIMIT_FREE:
-        st.warning(f"⚠️ Limite gratuite atteinte ({SAVE_LIMIT_FREE} uploads). Passez en mode premium pour continuer.")
-        return False
-    return True
-
-# ---------------- Prédiction image classique ----------------
-def predict_image(image, conf=0.85, show_labels=True):
-    if not check_limit("uploads_count"):
-        return None
-    np_img = np.array(image)
-    face_bbox = _largest_face_bbox(np_img)
-    if face_bbox is None:
-        st.warning("⚠️ Aucun visage humain détecté. Veuillez centrer le visage.")
-        return None
-    if np_img.shape[2] == 4:
-        np_img = cv2.cvtColor(np_img, cv2.COLOR_RGBA2BGR)
-    else:
-        np_img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
-    results = model.predict(source=np_img, conf=conf, verbose=False)
-    if len(results[0].boxes) == 0:
-        return None
-    annotated_image = results[0].plot(labels=show_labels)
-    out_path = os.path.join(SAVE_DIR, f"image_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png")
-    cv2.imwrite(out_path, annotated_image)
-    st.session_state.uploads_count += 1
-    return out_path
-
-# ---------------- Prédiction vidéo ----------------
-def predict_video(video_path, conf=0.85, show_labels=True):
-    if not check_limit("uploads_count"):
-        return None
-    cap = cv2.VideoCapture(video_path)
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out_path = os.path.join(SAVE_DIR, f"video_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp4")
-    fps = cap.get(cv2.CAP_PROP_FPS) or 30
-    width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    out = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
-    detections = 0
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        results = model.predict(frame, conf=conf, verbose=False)
-        if len(results[0].boxes) > 0:
-            detections += 1
-        annotated = results[0].plot(labels=show_labels)
-        out.write(annotated)
-    cap.release()
-    out.release()
-    if detections == 0:
-        return None
-    st.session_state.uploads_count += 1
-    return out_path
-
-# ---------------- Prédiction IRM ----------------
-def predict_image_irm(image, conf=0.8, show_labels=True):
-    if not check_limit("uploads_count_irm"):
-        return None
-    np_img = np.array(image)
-    if np_img.shape[2] == 4:
-        np_img = cv2.cvtColor(np_img, cv2.COLOR_RGBA2BGR)
-    else:
-        np_img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
-    results = model_irm.predict(source=np_img, conf=conf, verbose=False)
-    if results[0].masks is None or len(results[0].masks.data) == 0:
-        st.warning("⚠️ Aucun masque détecté par le modèle IRM.")
-        return None
-    annotated_image = results[0].plot(labels=show_labels)
-    out_path = os.path.join(SAVE_DIR, f"irm_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png")
-    cv2.imwrite(out_path, annotated_image)
-    st.session_state.uploads_count_irm += 1
-    return out_path
-
-# ---------------- Prédiction Stroke IRM ----------------
-def predict_image_stroke(image, conf=0.8, show_labels=True):
-    if not check_limit("uploads_count_stroke"):
-        return None
-    np_img = np.array(image)
-    if np_img.shape[2] == 4:
-        np_img = cv2.cvtColor(np_img, cv2.COLOR_RGBA2BGR)
-    else:
-        np_img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
-    results = model_stroke.predict(source=np_img, conf=conf, verbose=False)
-    if len(results[0].boxes) == 0:
-        st.warning("⚠️ Aucun AVC détecté par le modèle Stroke.")
+# --- CONFIGURATION ---
+MODEL_PATH = "best.pt"  # Jo aapki file list mein hai
+LOG_FILE = "scan_history.csv"
+
+# Model load karne ka function
+@st.cache_resource
+def load_my_model():
+    # Agar YOLOv8 hai toh ultralytics use karein, v5 hai toh torch.hub
+    try:
+        model = torch.hub.load('ultralytics/yolov5', 'custom', path=MODEL_PATH)
+        return model
+    except:
+        st.error("Model load nahi ho raha. Check karein ki best.pt sahi jagah hai.")
         return None
-    annotated_image = results[0].plot(labels=show_labels)
-    out_path = os.path.join(SAVE_DIR, f"stroke_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png")
-    cv2.imwrite(out_path, annotated_image)
-    st.session_state.uploads_count_stroke += 1
-    return out_path
-
-# ---------------- Interface Streamlit ----------------
-st.title("🧠 Stroke-IA  Détection AVC par IA")
-
-# ---------------- Sidebar ----------------
-st.sidebar.header("⚙️ Paramètres utilisateur")
-conf_threshold = st.sidebar.slider("Seuil de confiance (images/vidéos)", 0.1, 1.0, 0.85, 0.05, key="conf_slider")
-conf_threshold_irm = st.sidebar.slider("Seuil de confiance (IRM)", 0.1, 1.0, 0.8, 0.05, key="conf_slider_irm")
-conf_threshold_stroke = st.sidebar.slider("Seuil de confiance (Stroke IRM)", 0.1, 1.0, 0.8, 0.05, key="conf_slider_stroke")
-show_labels = st.sidebar.checkbox("Afficher les labels", value=True, key="labels_checkbox")
 
-st.sidebar.header("🔑 Premium / Essai")
-if not st.session_state.premium_access:
-    user_key = st.sidebar.text_input("Entrez votre clé premium :", type="password", key="premium_input")
-    if user_key == PREMIUM_KEY:
-        st.session_state.premium_access = True
-        st.sidebar.success("✅ Mode premium activé ! La limitation est levée.")
-        st.rerun()
-
-if not st.session_state.premium_access:
-    st.sidebar.info(f"📊 Utilisation gratuite images/vidéos : {st.session_state.uploads_count}/{SAVE_LIMIT_FREE}")
-    st.sidebar.info(f"📊 Utilisation gratuite IRM : {st.session_state.uploads_count_irm}/{SAVE_LIMIT_FREE}")
-    st.sidebar.info(f"📊 Utilisation gratuite Stroke IRM : {st.session_state.uploads_count_stroke}/{SAVE_LIMIT_FREE}")
-
-# ---------------- Upload vidéo ----------------
-st.header("🎥 Détection sur vidéo")
-video_file = st.file_uploader("Uploader une vidéo", type=["mp4", "mov"], key="video_uploader")
-if video_file and st.button("Analyser la vidéo", key="video_button"):
-    temp_path = os.path.join(SAVE_DIR, "temp_video.mp4")
-    with open(temp_path, "wb") as f:
-        f.write(video_file.read())
-    result_path = predict_video(temp_path, conf=conf_threshold, show_labels=show_labels)
-    if result_path is None:
-        st.success("✅ Aucun AVC détecté ou limite gratuite atteinte.")
+# Data auto-save karne ka function
+def auto_log_data(result_count):
+    new_entry = pd.DataFrame([[pd.Timestamp.now(), result_count]], columns=["Date", "Detections"])
+    if not os.path.isfile(LOG_FILE):
+        new_entry.to_csv(LOG_FILE, index=False)
     else:
-        st.video(result_path)
-
-# ---------------- Upload image ----------------
-st.header("🖼️ Détection sur image")
-image_file = st.file_uploader("Uploader une image", type=["jpg", "jpeg", "png"], key="image_uploader")
-if image_file and st.button("Analyser l'image", key="image_button"):
-    image = Image.open(image_file)
-    result_path = predict_image(image, conf=conf_threshold, show_labels=show_labels)
-    if result_path is None:
-        st.success("✅ Aucun AVC détecté ou limite gratuite atteinte.")
+        new_entry.to_csv(LOG_FILE, mode='a', header=False, index=False)
+
+# --- UI SETUP ---
+st.set_page_config(page_title="Stroke-IA Detector", layout="wide")
+st.title("🧠 Stroke-IA Real-time Analysis")
+
+tab1, tab2 = st.tabs(["🔍 Detection", "📊 Analytics Dashboard"])
+
+model = load_my_model()
+
+with tab1:
+    st.subheader("Upload for AI Scanning")
+    uploaded_file = st.file_uploader("Image ya Video select karein", type=['jpg', 'jpeg', 'png', 'mp4'])
+
+    if uploaded_file is not None and model is not None:
+        # Image Analysis
+        if uploaded_file.type.startswith('image'):
+            img = Image.open(uploaded_file)
+            results = model(img) # Model prediction
+            
+            # Show Result
+            st.image(np.squeeze(results.render()), caption="AI Prediction")
+            
+            # Auto-Save
+            det_count = len(results.pandas().xyxy[0])
+            if st.button("Save Result to Dashboard"):
+                auto_log_data(det_count)
+                st.success(f"Data Saved! {det_count} signs detected.")
+
+with tab2:
+    st.subheader("📈 Automatic Analysis History")
+    if os.path.exists(LOG_FILE):
+        df = pd.read_csv(LOG_FILE)
+        st.write("Aapko dobara CSV upload karne ki zaroorat nahi hai. Ye history hai:")
+        st.dataframe(df, use_container_width=True)
+        st.line_chart(df['Detections'])
     else:
-        st.image(result_path, caption="Image annotée", use_container_width=True)
-
-# ---------------- Upload IRM ----------------
-st.header("🧠 Détection CANCER par IRM")
-irm_file = st.file_uploader("Uploader une IRM", type=["jpg", "jpeg", "png"], key="irm_uploader")
-if irm_file and st.button("Analyser l'IRM", key="irm_button"):
-    irm_image = Image.open(irm_file)
-    result_path_irm = predict_image_irm(irm_image, conf=conf_threshold_irm, show_labels=show_labels)
-    if result_path_irm is None:
-        st.success("✅ Aucun résultat détecté ou limite gratuite atteinte.")
-    else:
-        st.image(result_path_irm, caption="IRM annotée", use_container_width=True)
-
-# ---------------- Upload IRM Stroke ----------------
-st.header("🧠 Détection AVC par IRM")
-stroke_file = st.file_uploader("Uploader une IRM pour Stroke", type=["jpg", "jpeg", "png"], key="stroke_uploader")
-if stroke_file and st.button("Analyser l'IRM Stroke", key="stroke_button"):
-    stroke_image = Image.open(stroke_file)
-    result_path_stroke = predict_image_stroke(stroke_image, conf=conf_threshold_stroke, show_labels=show_labels)
-    if result_path_stroke is None:
-        st.success("✅ Aucun résultat détecté ou limite gratuite atteinte.")
-    else:
-        st.image(result_path_stroke, caption="Stroke annotée", use_container_width=True)
-
-
-# Upload IRM 3D (cancer)
-st.header("🧠 Détection Tumeur (IRM 3D)")
-irm3d_files = st.file_uploader("Uploader 4 séquences (FLAIR, T1, T1CE, T2)", type=["nii", "nii.gz"], accept_multiple_files=True)
-if irm3d_files and st.button("Analyser IRM 3D"):
-    if len(irm3d_files) != 4:
-        st.error("⚠️ Merci d’uploader exactement 4 fichiers IRM (FLAIR, T1, T1CE, T2)")
-    else:
-        tmp_paths = []
-        for f in irm3d_files:
-            path = os.path.join(SAVE_DIR, f.name)
-            with open(path, "wb") as out:
-                out.write(f.read())
-            tmp_paths.append(path)
-        seg, report_text, (nii_path, report_path, mask_path) = irm_cancer_module.run(tmp_paths)
-        st.subheader("📝 Rapport automatique")
-        st.text(report_text)
-        if mask_path and os.path.exists(mask_path):
-            st.image(mask_path, caption="Segmentation annotée", use_container_width=True)
-
-# Disclaimer
-st.markdown(f"""
----
-👨‍💻 **Badsi Djilali** — Ingénieur Deep Learning  
-🚀 Créateur de **Stroke_IA_Detection**  
-
-⚠️ Démo technique, pas un avis médical.  
-© {datetime.now().year} — Badsi Djilali.
-""")
+        st.info("Abhi tak koi scan save nahi kiya gaya hai.")