Update app.py

app.py

@@ -1,11 +1,71 @@
+# import streamlit as st
+
+# # Set title of the app
+# st.title("Simple Streamlit App")
+
+# # Add text input
+# user_input = st.text_input("Enter your name:")
+
+# # Display the input value
+# if user_input:
+#     st.write(f"Hello, {user_input}!")
+
+
+
+
+
+
+
 import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+
+# Load the pre-trained models
+@st.cache_resource
+def load_models():
+    model1 = load_model('name_model_inception.h5')  # Update with your Hugging Face model path
+    model2 = load_model('type_model_inception.h5')  # Update with your Hugging Face model path
+    return model1, model2
+
+model1, model2 = load_models()
+
+# Label mappings
+label_map1 = {
+    0: "Banana", 1: "Cucumber", 2: "Grape", 3: "Kaki", 4: "Papaya",
+    5: "Peach", 6: "Pear", 7: "Pepper", 8: "Strawberry", 9: "Watermelon", 10: "Tomato"
+}
+
+label_map2 = {
+    0: "Good", 1: "Mild", 2: "Rotten"
+}
+
+# Streamlit app layout
+st.title("Fruit Classifier")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose an image of a fruit", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    img = Image.open(uploaded_file)
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+    
+    # Preprocess the image
+    img = img.resize((224, 224))  # Resize image to match the model input
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0  # Normalize the image
+
+    # Make predictions
+    pred1 = model1.predict(img_array)
+    pred2 = model2.predict(img_array)
 
-# Set title of the app
-st.title("Simple Streamlit App")
+    predicted_class1 = np.argmax(pred1, axis=1)
+    predicted_class2 = np.argmax(pred2, axis=1)
 
-# Add text input
-user_input = st.text_input("Enter your name:")
+    # Display results
+    st.write(f"**Type Detection**: {label_map1[predicted_class1[0]]}")
+    st.write(f"**Condition Detection**: {label_map2[predicted_class2[0]]}")
 
-# Display the input value
-if user_input:
-    st.write(f"Hello, {user_input}!")