Upload app.py

app.py

@@ -1,14 +1,92 @@
+# 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()
 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
+import os
 
 # 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',
@@ -17,11 +95,18 @@ class_names = [
     '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"])
+on_huggingface = os.environ.get("SPACE_ID") is not None
+
+
+st.title(" Flower Identifier")
+
+
+if on_huggingface:
+    st.warning("Real-time camera is not supported on Hugging Face Spaces. Please upload an image.")
+    mode = "Upload Image"
+else:
+    mode = st.radio("Choose input method:", ["Upload Image", "Real-Time Camera"])
 
 if mode == "Upload Image":
     st.markdown("### Upload an image of a flower")
@@ -41,8 +126,8 @@ if mode == "Upload Image":
             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}%**")
+            st.success(f" Predicted Flower: **{flower_name}**")
+            st.info(f"🔍 Confidence: **{confidence}%**")
 
 elif mode == "Real-Time Camera":
     st.markdown("### Real-Time Flower Recognition")
@@ -52,6 +137,7 @@ elif mode == "Real-Time Camera":
     cap = None
     if run:
         cap = cv2.VideoCapture(0)
+
         while run:
             ret, frame = cap.read()
             if not ret:
@@ -67,7 +153,6 @@ elif mode == "Real-Time Camera":
             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)