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app.py

@@ -75,20 +75,19 @@
 
 #         if cap:
 #             cap.release()
-import streamlit as st
+
+
+
+import gradio as gr
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 from PIL import Image
-import os
-
-# Check if running on Hugging Face Spaces
-on_huggingface = os.environ.get("SPACE_ID") is not None
 
-# Load model
+# Load the model
 model = tf.keras.models.load_model("model_n.keras")
 
-# Class names
+# Define class names
 class_names = [
     'Bush Clock Vine', 'Common Lanthana', 'Datura', 'Hibiscus', 'Jatropha', 'Marigold',
     'Nityakalyani', 'Rose', 'Yellow_Daisy', 'adathoda', 'banana', 'champaka', 'chitrak',
@@ -97,68 +96,35 @@ class_names = [
     'thumba', 'touch me not', 'tridax procumbens', 'wild_potato_vine'
 ]
 
-# Title
-st.title("🌼 Flower Identifier")
-
-# Choose mode
-if on_huggingface:
-    st.warning("Real-time camera is not supported on Hugging Face. Please upload an image.")
-    mode = "Upload Image"
-else:
-    mode = st.radio("Choose input method:", ["Upload Image", "Real-Time Camera"])
-
-# Upload image mode
-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}%**")
-
-# Real-time camera mode (local only)
-elif mode == "Real-Time Camera":
-    import cv2  # <- import only if needed
-    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]
-
-            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()
+# Prediction function
+def predict_flower(img):
+    image = img.convert("RGB")
+    image = tf.keras.preprocessing.image.img_to_array(image)
+    image = tf.cast(image, tf.float32)
+    image = tf.expand_dims(image, 0)
+
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction[0])
+    confidence = round(100 * np.max(prediction[0]), 2)
+    flower_name = class_names[predicted_class]
+
+    return f"🌼 Predicted Flower: {flower_name} ({confidence}%)"
+
+# Gradio interface
+title = "🌸 Flower Identifier using Deep Learning"
+description = "Upload an image or use your camera to identify a flower from 28 known classes."
+
+iface = gr.Interface(
+    fn=predict_flower,
+    inputs=[
+        gr.Image(type="pil", label="Upload or Capture Flower Image", source="upload", tool="editor")
+    ],
+    outputs="text",
+    title=title,
+    description=description,
+    live=False,
+    examples=None,
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -1,6 +1,7 @@
+gradio>=4.0.0
 streamlit>=1.30.0
+tensorflow>=2.10.0
 numpy>=1.22.0
-tensorflow>=2.19.0
 Pillow>=9.0.0
-opencv-python>=4.5.5
+opencv-python-headless>=4.5.5
 keras