Update app.py

app.py

@@ -1,14 +1,9 @@
 import gradio as gr
-import tensorflow
 import numpy as np
 import cv2 as cv
 import requests
 import time
-import os
 
-host = os.environ.get("host")
-code = os.environ.get("code")
-model = os.environ.get("model")
 data = None
 model = None
 image = None
@@ -18,7 +13,6 @@ labels = None
 print('START')
 np.set_printoptions(suppress=True)
 
-model = tensorflow.keras.models.load_model('keras_model.h5')
 data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 
 with open("labels.txt", "r") as file:
@@ -29,8 +23,13 @@ def classify(image_path):
         image_data = np.array(image_path)
         image_data = cv.resize(image_data, (224, 224))
         image_array = np.asarray(image_data)
-        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+        normalized_image_array = (image_array.astype(np float32) / 127.0) - 1
         data[0] = normalized_image_array
+
+        # Load the model within the classify function
+        import tensorflow as tf
+        model = tf.keras.models.load_model('keras_model.h5')
+
         prediction = model.predict(data)
         
         max_label_index = None
@@ -52,15 +51,14 @@ def classify(image_path):
             print(f'Maximum Prediction: {max_label} with a value of {round(max_prediction_value, 2)}')
 
             time.sleep(1)
-            print("\nWays to dispose this waste: " + max_label)
+            print("\nWays to dispose of this waste: " + max_label)
             payload = [
                 {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": "Give me the steps to dispose this waste in bulleting points 5 max: " + "Plastic"}
+                {"role": "user", "content": "Give me the steps to dispose of this waste in bullet points (5 max): " + "Plastic"}
             ]
 
             response = requests.post(host, json={
                 "messages": payload,
-                "model": model,
                 "temperature": 0.5,
                 "presence_penalty": 0,
                 "frequency_penalty": 0,