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@@ -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
+cs_p[[:space:]]-[[:space:]]Copy/code[[:space:]]pdf.pdf filter=lfs diff=lfs merge=lfs -text

cs_p - Copy/.gradio/certificate.pem

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cs_p - Copy/app.py

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+import joblib
+import numpy as np
+import gradio as gr
+
+# Load artifacts
+scaler = joblib.load("scaler.joblib")
+model  = joblib.load("svm_model.joblib")
+label_map = {0: "air", 1: "ground", 2: "naval"}
+
+def classify_uav(
+    engine_power, wing_span, max_speed, weight, payload_capacity,
+    sensor_quality, radar_signature, battery_life,
+    communication_range, altitude_capability
+):
+    X = np.array([[ 
+        engine_power, wing_span, max_speed, weight, payload_capacity,
+        sensor_quality, radar_signature, battery_life,
+        communication_range, altitude_capability
+    ]])
+    X_scaled = scaler.transform(X)
+    y_pred = model.predict(X_scaled)[0]
+    return label_map[y_pred]
+
+# Define some example rows
+sample_examples = [
+    # [engine_power, wing_span, max_speed, weight, payload_capacity,
+    #  sensor_quality, radar_signature, battery_life, communication_range, altitude_capability]
+    [250, 20, 300, 1200, 230, 6, 3.5, 160, 180, 10500],  # air
+    [180, 18, 220, 900, 200, 5, 4.0, 150, 150, 9000],     # ground
+    [300, 18, 350,  800, 180, 7, 2.5, 180, 210, 12000],   # air
+]
+
+
+iface = gr.Interface(
+    fn=classify_uav,
+    inputs=[
+        gr.Number(label="Engine Power"),
+        gr.Number(label="Wing Span"),
+        gr.Number(label="Max Speed"),
+        gr.Number(label="Weight"),
+        gr.Number(label="Payload Capacity"),
+        gr.Number(label="Sensor Quality"),
+        gr.Number(label="Radar Signature"),
+        gr.Number(label="Battery Life"),
+        gr.Number(label="Communication Range"),
+        gr.Number(label="Altitude Capability"),
+    ],
+    outputs=gr.Textbox(label="Predicted Target Type"),
+    title="UAV Target Classifier",
+    description="Enter UAV features to predict whether it’s targeting air, ground, or naval.",
+    examples=sample_examples,
+    examples_per_page=3
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)

cs_p - Copy/code pdf.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:743edb2f30a24209d57ae2f069f2e3526ac8835d97a5c63862da72207224e572
+size 11276758

cs_p - Copy/requirements.txt

@@ -0,0 +1,5 @@
+gradio
+scikit-learn
+joblib
+pandas
+numpy

cs_p - Copy/scaler.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cac4fa39152e51d52b530565717d4b15d519966a0cd8e5448647460a62cbb70c
+size 1351

cs_p - Copy/svm_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e433c8814b0d0c30a284c7376cc39b8b62877b2d846dacd7594ccbfe44fe723a
+size 187107