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| import gradio as gr | |
| 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_llm = os.environ.get("model") | |
| content = os.environ.get("content") | |
| state = os.environ.get("state") | |
| data = None | |
| model = None | |
| image = None | |
| prediction = None | |
| labels = None | |
| print('START') | |
| np.set_printoptions(suppress=True) | |
| 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, text_input): | |
| if text_input == code: | |
| output = [] # Create an empty list for output | |
| 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 | |
| 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 | |
| max_prediction_value = -1 | |
| 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 # Update max_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 of this waste: " + max_label) | |
| payload = [ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": "Give me the steps to dispose of this waste in bullet points (5 max): " + max_label} | |
| ] | |
| response = requests.post(host, json={ | |
| "messages": payload, | |
| "model": model_llm, | |
| "temperature": 0.5, | |
| "presence_penalty": 0, | |
| "frequency_penalty": 0, | |
| "top_p": 1 | |
| }).json() | |
| reply = response["choices"][0]["message"]["content"] | |
| output.append({"type": max_label, "prediction_value": rounded_value, "content": reply}) | |
| return output # Return the populated output list | |
| else: | |
| return "Unauthorized" | |
| iface = gr.Interface( | |
| fn=classify, | |
| inputs=[gr.inputs.Image(), "text"], | |
| outputs=gr.outputs.JSON(), # Output as JSON | |
| title="Waste Classifier", | |
| description="Upload an image to classify and get disposal instructions." | |
| ) | |
| iface.launch() | |