Fix Dependencies

app.py

@@ -1,19 +1,41 @@
+# app.py
+
 import gradio as gr
 import tensorflow as tf
 import numpy as np
 from PIL import Image
 import os
 
+# Suppress TensorFlow logging for cleaner logs
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
 # Load labels
 with open('tensorflow/labels.txt', 'r') as f:
     labels = f.read().splitlines()
 
-# Load TensorFlow model
-model = tf.keras.models.load_model('tensorflow/model.pb')
+# Function to load the frozen TensorFlow graph
+def load_frozen_graph(pb_file_path):
+    with tf.io.gfile.GFile(pb_file_path, 'rb') as f:
+        graph_def = tf.compat.v1.GraphDef()
+        graph_def.ParseFromString(f.read())
+    
+    with tf.Graph().as_default() as graph:
+        tf.import_graph_def(graph_def, name='')
+    return graph
+
+# Load the TensorFlow model
+MODEL_DIR = 'tensorflow/'
+graph = load_frozen_graph(os.path.join(MODEL_DIR, 'model.pb'))
+sess = tf.compat.v1.Session(graph=graph)
+
+# Identify input and output tensor names
+# You may need to adjust these based on your model's actual tensor names
+input_tensor = graph.get_tensor_by_name('input:0')  # Replace 'input:0' with your actual input tensor name
+output_tensor = graph.get_tensor_by_name('output:0')  # Replace 'output:0' with your actual output tensor name
 
 def preprocess_image(image):
     # Resize image to the size expected by the model
-    target_size = (224, 224)  # Replace with your model's expected input size
+    target_size = (320, 320)  # Replace with your model's expected input size
     image = image.resize(target_size)
     image = np.array(image)
     image = image / 255.0  # Normalize if required
@@ -22,7 +44,7 @@ def preprocess_image(image):
 def predict(image):
     image = preprocess_image(image)
     image = np.expand_dims(image, axis=0)  # Add batch dimension
-    predictions = model.predict(image)
+    predictions = sess.run(output_tensor, feed_dict={input_tensor: image})
     predicted_index = np.argmax(predictions, axis=1)[0]
     predicted_label = labels[predicted_index]
     confidence = predictions[0][predicted_index] * 100

requirements.txt

@@ -1,4 +1,4 @@
 gradio
-tensorflow==2.11.0  # Replace with your TensorFlow version
+tensorflow==2.11.0  # Ensure this matches the TensorFlow version used during model training
 Pillow
-numpy
+numpy<2.0.0