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.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
+model_n.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,77 @@
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from PIL import Image
+import cv2
+
+# Load the model
+model = tf.keras.models.load_model("model_n.keras")
+
+# Define class names
+class_names = [
+    'Bush Clock Vine', 'Common Lanthana', 'Datura', 'Hibiscus', 'Jatropha', 'Marigold',
+    'Nityakalyani', 'Rose', 'Yellow_Daisy', 'adathoda', 'banana', 'champaka', 'chitrak',
+    'crown flower', "four o'clock flower", 'honeysuckle', 'indian mallow', 'malabar melastome',
+    'nagapoovu', 'pinwheel flower', 'shankupushpam', 'spider lily', 'sunflower', 'thechi',
+    'thumba', 'touch me not', 'tridax procumbens', 'wild_potato_vine'
+]
+
+# Title
+st.title("Flower Identifier")
+
+# Choose mode
+mode = st.radio("Choose input method:", ["Upload Image", "Real-Time Camera"])
+
+if mode == "Upload Image":
+    st.markdown("### Upload an image of a flower")
+    img = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+    
+    if img is not None:
+        st.image(img, caption="Uploaded Image", use_column_width=True)
+
+        image = Image.open(img).convert("RGB")
+        image = tf.keras.preprocessing.image.img_to_array(image)
+        image = tf.cast(image, tf.float32)
+        image = tf.expand_dims(image, 0)
+
+        if st.button("Identify Flower"):
+            prediction = model.predict(image)
+            predicted_class = np.argmax(prediction[0])
+            confidence = round(100 * np.max(prediction[0]), 2)
+            flower_name = class_names[predicted_class]
+
+            st.success(f"Predicted Flower: **{flower_name}**")
+            st.info(f"Confidence: **{confidence}%**")
+
+elif mode == "Real-Time Camera":
+    st.markdown("### Real-Time Flower Recognition")
+    run = st.checkbox('Start Camera')
+    FRAME_WINDOW = st.image([])
+
+    cap = None
+    if run:
+        cap = cv2.VideoCapture(0)
+        while run:
+            ret, frame = cap.read()
+            if not ret:
+                st.warning("Failed to access camera.")
+                break
+
+            img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            img_array = tf.keras.preprocessing.image.img_to_array(img_rgb)
+            img_array = tf.expand_dims(tf.cast(img_array, tf.float32), 0)
+
+            predictions = model.predict(img_array)
+            predicted_class = np.argmax(predictions[0])
+            confidence = round(100 * np.max(predictions[0]), 2)
+            flower_name = class_names[predicted_class]
+
+            # Annotate frame
+            cv2.putText(frame, f"{flower_name} ({confidence}%)", (10, 30), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
+
+            FRAME_WINDOW.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+        if cap:
+            cap.release()

model_n.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4981c52739db96f17a22ed4e37faa78e62ddda5bf49f0309dd2eb43039d5685
+size 2308223

requirements.txt

@@ -0,0 +1,6 @@
+streamlit>=1.30.0
+numpy>=1.22.0
+tensorflow>=2.19.0
+Pillow>=9.0.0
+opencv-python>=4.5.5
+keras