Igihingwa

docs/the-basics.md

@@ -38,3 +38,156 @@ export const PortChooser = () => {
 };
 
 <PortChooser />
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+import matplotlib.pyplot as plt
+
+# Load dataset (PlantVillage or your custom data)
+img_size = (128, 128)
+batch_size = 32
+
+train_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    "data/train",
+    image_size=img_size,
+    batch_size=batch_size
+)
+
+val_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    "data/val",
+    image_size=img_size,
+    batch_size=batch_size
+)
+
+# Normalize
+normalization_layer = layers.Rescaling(1./255)
+train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
+val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))
+
+# Model
+model = keras.Sequential([
+    layers.Conv2D(32, 3, activation='relu', input_shape=(128,128,3)),
+    layers.MaxPooling2D(),
+    layers.Conv2D(64, 3, activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(128, 3, activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Flatten(),
+    layers.Dense(128, activation='relu'),
+    layers.Dense(train_ds.cardinality().numpy(), activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+history = model.fit(train_ds, validation_data=val_ds, epochs=10)
+
+# Save model
+model.save("crop_disease_model.h5")
+
+# Convert to TensorFlow Lite
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+tflite_model = converter.convert()
+with open("crop_disease_model.tflite", "wb") as f:
+    f.write(tflite_model)package com.example.cropdoctor
+
+import android.app.Activity
+import android.graphics.Bitmap
+import android.os.Bundle
+import android.widget.Button
+import android.widget.ImageView
+import android.widget.TextView
+import org.tensorflow.lite.Interpreter
+import java.nio.ByteBuffer
+import java.nio.ByteOrder
+
+class MainActivity : Activity() {
+    private lateinit var resultText: TextView
+    private lateinit var imageView: ImageView
+    private lateinit var tflite: Interpreter
+
+    override fun onCreate(savedInstanceState: Bundle?) {
+        super.onCreate(savedInstanceState)
+        setContentView(R.layout.activity_main)
+
+        resultText = findViewById(R.id.resultText)
+        imageView = findViewById(R.id.imageView)
+        val scanBtn: Button = findViewById(R.id.scanBtn)
+
+        // Load model
+        val model = assets.open("crop_disease_model.tflite").readBytes()
+        tflite = Interpreter(model)
+
+        scanBtn.setOnClickListener {
+            // TODO: Capture photo from camera
+            val bitmap: Bitmap = captureImage() // your function
+            val result = classifyImage(bitmap)
+            resultText.text = result
+        }
+    }
+
+    private fun classifyImage(bitmap: Bitmap): String {
+        val inputSize = 128
+        val buffer = ByteBuffer.allocateDirect(4 * inputSize * inputSize * 3)
+        buffer.order(ByteOrder.nativeOrder())
+        val resized = Bitmap.createScaledBitmap(bitmap, inputSize, inputSize, true)
+
+        for (y in 0 until inputSize) {
+            for (x in 0 until inputSize) {
+                val px = resized.getPixel(x, y)
+                buffer.putFloat(((px shr 16 and 0xFF) / 255.0f))
+                buffer.putFloat(((px shr 8 and 0xFF) / 255.0f))
+                buffer.putFloat(((px and 0xFF) / 255.0f))
+            }
+        }
+
+        val output = Array(1) { FloatArray(4) } // Example: 4 classes
+        tflite.run(buffer, output)
+
+        val labels = listOf("Healthy", "Leaf Blight", "Rust", "Mosaic")
+        val maxIdx = output[0].indices.maxByOrNull { output[0][it] } ?: -1
+        return labels[maxIdx]
+    }
+}<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    android:orientation="vertical"
+    android:gravity="center"
+    android:padding="16dp"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent">
+
+    <ImageView
+        android:id="@+id/imageView"
+        android:layout_width="200dp"
+        android:layout_height="200dp"
+        android:scaleType="centerCrop"/>
+
+    <Button
+        android:id="@+id/scanBtn"
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:text="Scan My Crop"/>
+
+    <TextView
+        android:id="@+id/resultText"
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:text="Result will show here"
+        android:textSize="18sp"/>
+</LinearLayout>from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+disease_reports = []
+
+@app.route("/report", methods=["POST"])
+def report():
+    data = request.json
+    disease_reports.append(data)
+    return jsonify({"status": "success"}), 200
+
+@app.route("/outbreaks", methods=["GET"])
+def outbreaks():
+    return jsonify(disease_reports)
+
+if __name__ == "__main__":
+    app.run(debug=True)