Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+from ultralytics import YOLO
+import cv2, os
+from datetime import datetime
+import numpy as np
+
+# ---------------- Config générale ----------------
+MODEL_PATH = "best.pt"
+SAVE_DIR = os.path.join("/tmp", "results")
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+# Charger le modèle
+model = YOLO(MODEL_PATH)
+
+# ---------------- Fonctions utilitaires ----------------
+def predict_image(image, conf=0.25, show_labels=True):
+    image = np.array(image)
+    if image.shape[2] == 4:
+        image = cv2.cvtColor(image, cv2.COLOR_RGBA2BGR)
+    else:
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    results = model.predict(source=image, conf=conf, verbose=False)
+    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)
+    return out_path
+
+def predict_video(video_path, conf=0.25, show_labels=True):
+    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))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        results = model.predict(frame, conf=conf, verbose=False)
+        annotated = results[0].plot(labels=show_labels)
+        out.write(annotated)
+
+    cap.release()
+    out.release()
+    return out_path
+
+# ---------------- Interface Streamlit ----------------
+st.title("🧠 Stroke-IA – Détection AVC par IA")
+
+# Sidebar (paramètres utilisateur)
+st.sidebar.header("⚙️ Paramètres")
+conf_threshold = st.sidebar.slider("Seuil de confiance", 0.1, 1.0, 0.25, 0.05)
+show_labels = st.sidebar.checkbox("Afficher les labels", value=True)
+
+st.sidebar.header("📂 Exemples rapides")
+if st.sidebar.button("Tester une image exemple"):
+    if os.path.exists("example.jpg"):
+        img = Image.open("example.jpg")
+        path = predict_image(img, conf=conf_threshold, show_labels=show_labels)
+        st.image(path, caption="Exemple annoté", use_column_width=True)
+    else:
+        st.warning("⚠️ Aucun fichier example.jpg trouvé dans le projet.")
+
+if st.sidebar.button("Tester une vidéo exemple"):
+    if os.path.exists("example.mp4"):
+        path = predict_video("example.mp4", conf=conf_threshold, show_labels=show_labels)
+        st.video(path)
+    else:
+        st.warning("⚠️ Aucun fichier example.mp4 trouvé dans le projet.")
+
+# Section vidéo upload
+st.header("🎥 Détection sur vidéo")
+video_file = st.file_uploader("Uploader une vidéo (mp4, mov, etc.)", type=["mp4", "mov"])
+if video_file and st.button("Analyser la vidéo"):
+    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)
+    st.video(result_path)
+
+# Section image upload
+st.header("🖼️ Détection sur image")
+image_file = st.file_uploader("Uploader une image", type=["jpg", "jpeg", "png"])
+if image_file and st.button("Analyser l'image"):
+    image = Image.open(image_file)
+    result_path = predict_image(image, conf=conf_threshold, show_labels=show_labels)
+    st.image(result_path, caption="Image annotée", use_column_width=True)
+
+# Disclaimer
+st.markdown(f"""
+---
+⚠️ **Disclaimer :** Stroke-IA est une démo technique, pas un avis médical.  
+© {datetime.now().year} — Badsi Djilali.
+""")