slight change

app.py

@@ -1,20 +1,15 @@
 import streamlit as st
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
+# import tensorflow as tf  # not needed just to infer
+from tf_keras.models import load_model            
+from tensorflow.keras.preprocessing import image  
 from tensorflow.keras.applications.resnet50 import preprocess_input
 import numpy as np
 from PIL import Image
 
-# --- Load Your Model and Class Names ---
-# Use st.cache_resource to load the model only once
 @st.cache_resource
 def load_my_model():
-    # Make sure this file name matches your model file
-    model = load_model('resnet50_dryfruits.h5') 
-    return model
+    return load_model("resnet50_dryfruits.h5", compile=False)  
 
-# --- This is the updated dictionary based on your list ---
 class_names = {
     0: 'AlmondGrade_A',
     1: 'CashewGrade_B',
@@ -26,31 +21,25 @@ class_names = {
     7: 'WalnutGrade_A',
     8: 'CashewGrade_C'
 }
-# --------------------------------------------------------
 
 model = load_my_model()
 
-# --- App Interface ---
 st.title("Dry Fruit Quality Grader")
 st.write("Upload an image of a dry fruit, and the model will predict its grade.")
 
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # 1. Preprocess the image
-    img = Image.open(uploaded_file).convert('RGB') # Ensure 3 channels
+    img = Image.open(uploaded_file).convert("RGB")
     img = img.resize((224, 224))
     img_array = image.img_to_array(img)
     img_batch = np.expand_dims(img_array, axis=0)
     img_preprocessed = preprocess_input(img_batch)
-    
-    # 2. Make prediction
-    prediction = model.predict(img_preprocessed)
-    predicted_index = np.argmax(prediction[0])
-    predicted_class_name = class_names[predicted_index]
-    confidence = np.max(prediction[0])
-    
-    # 3. Display results
+
+    preds = model.predict(img_preprocessed)
+    idx = int(np.argmax(preds[0]))
+    confidence = float(np.max(preds[0]))
+
     st.image(img, caption="Uploaded Image", use_column_width=True)
-    st.markdown(f"## Prediction: **{predicted_class_name}**")
+    st.markdown(f"## Prediction: **{class_names[idx]}**")
     st.markdown(f"### Confidence: **{confidence * 100:.2f}%**")

requirements.txt

@@ -1,3 +1,6 @@
-tensorflow==2.15.0
-streamlit
-Pillow
+streamlit==1.39.0
+tensorflow-cpu==2.17.0
+tf-keras==2.17.0
+h5py>=3.10
+Pillow
+numpy