List tensors

app.py

@@ -2,69 +2,36 @@
 
 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()
-
-# 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:
+def list_tensor_names():
+    # Load the frozen TensorFlow graph
+    MODEL_DIR = 'tensorflow/'
+    MODEL_PATH = os.path.join(MODEL_DIR, 'model.pb')
+    
+    with tf.io.gfile.GFile(MODEL_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 = (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
-    return image
-
-def predict(image):
-    image = preprocess_image(image)
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    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
-    return {predicted_label: round(confidence, 2)}
+    
+    tensor_names = [op.name for op in graph.get_operations()]
+    return "\n".join(tensor_names)
 
 # Define Gradio interface
-title = "JunkWaxHero 🦸‍♂️ - Baseball Card Set Identifier"
-description = "Upload an image of a baseball card, and JunkWaxHero will identify the set it belongs to with high accuracy."
+title = "JunkWaxHero 🦸‍♂️ - Tensor Names Inspector"
+description = "This interface lists all tensor names in the TensorFlow model to help identify the correct input and output tensors."
 
 iface = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(type="pil"),
-    outputs=gr.outputs.Label(num_top_classes=1),
+    fn=list_tensor_names,
+    inputs=None,
+    outputs="text",
     title=title,
     description=description,
-    examples=[
-        ["examples/card1.jpg"],
-        ["examples/card2.jpg"],
-        ["examples/card3.jpg"]
-    ],
     allow_flagging="never"
 )