Update Gradio Examples.py

Gradio Examples.py

@@ -1,106 +1,3 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# #### Gradio Comparing Transfer Learning Models
-
-# In[1]:
-
-
-import tensorflow as tf
-print(tf.__version__)
-
-
-# In[2]:
-
-
-pip install gradio==1.6.0
-
-
-# In[3]:
-
-
-pip install MarkupSafe==2.1.1
-
-
-# In[1]:
-
-
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import requests
-
-
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
-
-mobile_net = tf.keras.applications.MobileNetV2()
-inception_net = tf.keras.applications.InceptionV3()
-
-
-# In[2]:
-
-
-def classify_image_with_mobile_net(im):
-    im = Image.fromarray(im.astype('uint8'), 'RGB')
-    im = im.resize((224, 224))
-    arr = np.array(im).reshape((-1, 224, 224, 3))
-    arr = tf.keras.applications.mobilenet.preprocess_input(arr)
-    prediction = mobile_net.predict(arr).flatten()
-    return {labels[i]: float(prediction[i]) for i in range(1000)}
-    
-
-
-# In[3]:
-
-
-def classify_image_with_inception_net(im):
-    # Resize the image to
-    im = Image.fromarray(im.astype('uint8'), 'RGB')
-    im = im.resize((299, 299))
-    arr = np.array(im).reshape((-1, 299, 299, 3))
-    arr = tf.keras.applications.inception_v3.preprocess_input(arr)
-    prediction = inception_net.predict(arr).flatten()
-    return {labels[i]: float(prediction[i]) for i in range(1000)}
-
-
-# In[4]:
-
-
-imagein = gr.inputs.Image()
-label = gr.outputs.Label(num_top_classes=3)
-sample_images = [
-                 ["monkey.jpg"],
-                 ["sailboat.jpg"],
-                 ["bicycle.jpg"],
-                 ["download.jpg"],
-]
-
-
-# In[6]:
-
-
-gr.Interface(
-    [classify_image_with_mobile_net, classify_image_with_inception_net],
-    imagein,
-    label,
-    title="MobileNet vs. InceptionNet",
-    description="""Let's compare 2 state-of-the-art machine learning models that classify images into one of 1,000 categories: MobileNet (top),
-          a lightweight model that has an accuracy of 0.704, vs. InceptionNet
-          (bottom), a much heavier model that has an accuracy of 0.779.""",
-    examples=sample_images).launch()
-
-
-# In[6]:
-
-
-pip install transformers
-
-
-# In[6]:
-
 
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -145,102 +42,5 @@ iface = gr.Interface(
 iface.launch()
 
 
-# In[17]:
-
-
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-from torchvision import transforms
-from io import BytesIO
-from PIL import Image
-
-# Define the available models and datasets
-models = {
-    "Model 1": {
-        "model_name": "bert-base-uncased",
-        "tokenizer": None,
-        "model": None
-    },
-    "Model 2": {
-        "model_name": "distilbert-base-uncased",
-        "tokenizer": None,
-        "model": None
-    },
-    # Add more models as needed
-}
-
-datasets = {
-    "Dataset 1": {
-        "name": "imdb",
-        "split": "test",
-        "features": ["text"],
-    },
-    "Dataset 2": {
-        "name": "ag_news",
-        "split": "test",
-        "features": ["text"],
-    },
-    # Add more datasets as needed
-}
-
-# Load models
-for model_key, model_info in models.items():
-    tokenizer = AutoTokenizer.from_pretrained(model_info["model_name"])
-    model = AutoModelForSequenceClassification.from_pretrained(model_info["model_name"])
-    model_info["tokenizer"] = tokenizer
-    model_info["model"] = model
-
-# Set the device to GPU if available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-for model_info in models.values():
-    model_info["model"].to(device)
-
-# Define the preprocessing function
-def preprocess(image_file):
-    image = Image.open(BytesIO(image_file.read())).convert("RGB")
-    preprocess_transform = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
-    image = preprocess_transform(image)
-    image = image.unsqueeze(0)
-    return image.to(device)
-
-# Define the prediction function
-def predict(image_file, model_key):
-    model_info = models[model_key]
-    tokenizer = model_info["tokenizer"]
-    model = model_info["model"]
-
-    image = preprocess(image_file)
-
-    with torch.no_grad():
-        outputs = model(image)
-
-    predictions = outputs.logits.argmax(dim=1)
-
-    return predictions.item()
-
-def classify_image(image, model_key):
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
-    image_file = BytesIO()
-    image.save(image_file, format="JPEG")
-    prediction = predict(image_file=image_file, model_key=model_key)
-    return prediction
-
-iface = gr.Interface(fn=classify_image,
-                     inputs=["image", gr.inputs.Dropdown(list(models.keys()), label="Model")],
-                     outputs="text",
-                     title="Image Classification",
-                     description="Classify images using Hugging Face models")
-
-iface.launch()
-
-
-# In[ ]:
-
-