Update app.py

app.py

@@ -1,7 +1,81 @@
 import gradio as gr
+import tensorflow
+import numpy as np
+import cv2 as cv
+import requests
+import time
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+host = os.environ.get("host")
+code = os.environ.get("code")
+model = os.environ.get("model")
+data = None
+model = None
+image = None
+prediction = None
+labels = None
+max_label_index = None
+max_prediction_value = -1
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+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:
+    labels = file.read().splitlines()
+
+def classify(image_path):
+    try:
+        image = cv.imread(image_path)
+        image = cv.resize(image, (224, 224))
+        image_array = np.asarray(image)
+        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+        data[0] = normalized_image_array
+        prediction = model.predict(data)
+
+        print('Prediction')
+
+        for i, label in enumerate(labels):
+            prediction_value = float(prediction[0][i])
+            rounded_value = round(prediction_value, 2)
+            print(f'{label}: {rounded_value}')
+
+            if prediction_value > max_prediction_value:
+                max_label_index = i
+                max_prediction_value = prediction_value
+
+        if max_label_index is not None:
+            max_label = labels[max_label_index].split(' ', 1)[1]
+            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)
+            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"}
+            ]
+
+            response = requests.post(host, json={
+                "messages": payload,
+                "model": model,
+                "temperature": 0.5,
+                "presence_penalty": 0,
+                "frequency_penalty": 0,
+                "top_p": 1
+            }).json()
+
+            return response["choices"][0]["message"]["content"]
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+iface = gr.Interface(
+    fn=classify,
+    inputs="text",
+    outputs="text",
+    title="Waste Classifier",
+    description="Upload an image to classify and get disposal instructions."
+)
+iface.launch()