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Create app.py
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app.py
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import
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import openai
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import
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pdf_file = pdf_file.name
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loader = PyPDFLoader(pdf_file)
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docs = loader.load_and_split()
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chain = load_summarize_chain(llm, chain_type="map_reduce")
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summary = chain.run(docs)
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return summary
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main()
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import gradio as gr
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import json
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import requests
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import torch
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import torchvision
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import torchvision.models as models
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from torchvision import datasets, transforms
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from torchvision.models import mobilenet_v2
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from torch import nn, optim
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from torch.utils.data import DataLoader, TensorDataset
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from tqdm.auto import tqdm
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from jcopdl.callback import Callback, set_config
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import pandas as pd
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import numpy as np
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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device
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import openai
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import os
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import time
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from pathlib import Path
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from PIL import Image
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import io
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import cv2
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torch.manual_seed(0)
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class CustomMobileNetv2(nn.Module):
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def __init__(self, output_size):
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super().__init__()
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self.mnet = mobilenet_v2(pretrained=True)
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self.freeze()
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self.mnet.classifier = nn.Sequential(
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nn.Linear(1280, output_size),
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nn.LogSoftmax()
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)
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def forward(self, x):
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return self.mnet(x)
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def freeze(self):
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for param in self.mnet.parameters():
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param.requires_grad = False
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def unfreeze(self):
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for param in self.mnet.parameters():
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param.requires_grad = True
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kue_lokal_model = torch.load('rickyig_mobilenetv2_kue_lokal_classifier_entire_model.pth', map_location=torch.device('cpu'))
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dict_for_inference = {0: 'kue dadar gulung',
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1: 'kue kastengel',
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2: 'kue klepon',
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3: 'kue lapis',
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4: 'kue lumpur',
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5: 'kue putri salju',
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6: 'kue risoles',
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7: 'kue serabi'}
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def get_completion(prompt, model="gpt-3.5-turbo"):
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messages = [{"role": "user", "content": prompt}]
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response = openai.ChatCompletion.create(
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model=model,
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messages=messages,
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temperature=0,
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)
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return response.choices[0].message["content"]
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def get_response(classify_result):
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prompt = "Apa itu {} dan sebutkan resep dari {}.".format(classify_result, classify_result)
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response = get_completion(prompt)
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return response
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def classify_image(input_image):
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kue_lokal_model.eval()
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image_for_testing = input_image
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img = Image.open(image_for_testing)
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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])
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input_data = transform(img).unsqueeze(0).to(device='cpu')
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class_to_label = dict_for_inference
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with torch.no_grad():
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output = kue_lokal_model(input_data)
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probs = torch.nn.functional.softmax(output, dim=1)
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conf, predicted_class = torch.max(probs, 1)
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# Create a dictionary of class labels and their probabilities
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output_dict = {"predicted_label": class_to_label[predicted_class.item()], "probability": conf.item()}
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# Convert the dictionary to JSON format
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output_json = json.dumps(output_dict)
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output_bentuk_text = "Hasil Klasifikasi Gambar \nKue : {} \nProbability: {:.2f}%".format(class_to_label[predicted_class.item()], conf.item()*100)
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output_response = get_response(class_to_label[predicted_class.item()])
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return output_json, output_bentuk_text, output_response
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# Create a Gradio interface
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input_image = gr.Image(label="input_image", type="filepath")
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output_json = gr.JSON(label="Output (JSON)")
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output_bentuk_text = gr.Textbox(label="Hasil Output")
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output_response = gr.Textbox(label="Resep Kue")
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example_input_image = "3.jpg"
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interface = gr.Interface(
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fn=classify_image,
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inputs=input_image,
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outputs=[output_json, output_bentuk_text, output_response], # Use JSON output
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title="Resep Kue Lokal",
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examples=[
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[example_input_image]
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],
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description="Unggah foto kue lokal dan dapatkan hasil klasifikasi gambar beserta resep kue.<br>Kue yang tersedia: kue dadar gulung, kue kastengel, kue klepon, kue lapis, kue lumpur, kue putri salju, kue risoles, kue serabi.",
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)
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# Start the Gradio app
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interface.launch(share=True, debug=True)
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# ---- End of your code ----
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