Upload 5 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Custom_model.keras filter=lfs diff=lfs merge=lfs -text

Custom_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5781aa28b54fa7fd44270ee80251b8865eaf72eb39536222f0b95963190a3e6
+size 11650436

Dockerfile

@@ -1,20 +1,12 @@
-FROM python:3.13.5-slim
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+FROM python:3.10
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "streamlit_app.py", "--server.port=7860", "--server.address=0.0.0.0"]

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:395c0ab605717d9418b5c50662a7010067bd39f520a0a812ef9aed9d062a9670
+size 6216234

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+streamlit
+ultralytics
+tensorflow==2.13.0
+opencv-python-headless
+pillow
+numpy

streamlit_app.py

@@ -0,0 +1,89 @@
+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import tempfile
+import tensorflow as tf
+from io import BytesIO
+
+# Page Config
+st.set_page_config(page_title="Drone vs Bird", layout="wide")
+
+st.markdown(
+    "<h1 style='text-align:center;'>Drone vs Bird Detection</h1>",
+    unsafe_allow_html=True
+)
+
+# Load models (cached)
+@st.cache_resource
+def load_models():
+    model = tf.keras.models.load_model("Custom_model.keras")
+    yolomodel = YOLO("best.pt")   #  FIXED PATH
+    return model, yolomodel
+
+model, yolomodel = load_models()
+
+class_names = ["Bird", "Drone"]
+
+# Sidebar
+uploaded_file = st.sidebar.file_uploader("Upload Image", type=["jpg","png","jpeg"])
+run_mobilenet = st.sidebar.button("Run Classification")
+run_yolo = st.sidebar.button("Run Detection")
+
+# Main UI
+if uploaded_file:
+
+    col1, col2 = st.columns([1.2,1])
+
+    with col1:
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_container_width=True)
+
+        temp = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
+        image.save(temp.name)
+
+    with col2:
+
+        # ✅ MobileNet
+        if run_mobilenet:
+            img = image.resize((224,224))
+            img_array = np.array(img)/255.0
+            img_array = np.expand_dims(img_array, axis=0)
+
+            preds = model.predict(img_array)
+            label = class_names[int(preds[0][0] >= 0.5)]
+
+            st.success(f"Prediction: {label}")
+
+        # ✅ YOLO
+        if run_yolo:
+            results = yolomodel.predict(temp.name, conf=0.25)
+
+            annotated = results[0].plot()
+            annotated = annotated[:,:,::-1]
+
+            st.image(annotated, caption="Detection", use_container_width=True)
+
+            detections = results[0].boxes
+
+            if len(detections) == 0:
+                st.warning("No objects detected")
+            else:
+                for box in detections:
+                    cls = int(box.cls[0])
+                    conf = float(box.conf[0])*100
+                    st.write(f"{yolomodel.names[cls]} — {conf:.2f}%")
+
+            # Download
+            pil_img = Image.fromarray(annotated)
+            buf = BytesIO()
+            pil_img.save(buf, format="PNG")
+
+            st.download_button(
+                "Download Result",
+                buf.getvalue(),
+                file_name="result.png"
+            )
+
+else:
+    st.info("Upload an image to start")