Update app.py

app.py

@@ -1,15 +1,8 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-from PIL import Image, ImageEnhance
-import torch
-import os
-import numpy as np
-from torch.autograd import Variable
-from torchvision import transforms
-import torch.nn.functional as F
 
 # --- Model 1: AI Chatbot Setup ---
-client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3") # HuggingFaceH4/zephyr-7b-beta
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
 
 # Personalities for AI Chatbot
 PERSONALITIES = {
@@ -82,117 +75,7 @@ def generate_inspiration(history):
 def clear_conversation():
     return [], ""
 
-
-
-
-#######
-
-os.system("git clone https://github.com/xuebinqin/DIS")
-os.system("mv products/DIS/IS-Net/* .")
-
-from data_loader_cache import normalize, im_reader, im_preprocess 
-from models import *
-
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-if not os.path.exists("saved_models"):
-    os.mkdir("saved_models")
-    os.system("mv products/isnet.pth saved_models/")
-
-class GOSNormalize(object):
-    def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, image):
-        image = normalize(image, self.mean, self.std)
-        return image
-
-transform = transforms.Compose([GOSNormalize([0.5, 0.5, 0.5], [1.0, 1.0, 1.0])])
-
-def load_image(im_path, hypar):
-    im = im_reader(im_path)
-    im, im_shp = im_preprocess(im, hypar["cache_size"])
-    im = torch.divide(im, 255.0)
-    shape = torch.from_numpy(np.array(im_shp))
-    return transform(im).unsqueeze(0), shape.unsqueeze(0)
-
-def build_model(hypar, device):
-    net = hypar["model"]
-    if hypar["model_digit"] == "half":
-        net.half()
-        for layer in net.modules():
-            if isinstance(layer, nn.BatchNorm2d):
-                layer.float()
-
-    net.to(device)
-    if hypar["restore_model"] != "":
-        net.load_state_dict(torch.load(hypar["model_path"] + "/" + hypar["restore_model"], map_location=device))
-    net.eval()
-    return net
-
-def predict(net, inputs_val, shapes_val, hypar, device):
-    net.eval()
-    if hypar["model_digit"] == "full":
-        inputs_val = inputs_val.type(torch.FloatTensor)
-    else:
-        inputs_val = inputs_val.type(torch.HalfTensor)
-
-    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device)
-    ds_val = net(inputs_val_v)[0]
-    pred_val = ds_val[0][0, :, :, :]
-    pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val, 0), (shapes_val[0][0], shapes_val[0][1]), mode='bilinear'))
-
-    ma = torch.max(pred_val)
-    mi = torch.min(pred_val)
-    pred_val = (pred_val - mi) / (ma - mi)
-
-    if device == 'cuda': torch.cuda.empty_cache()
-    return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)
-
-hypar = {}
-hypar["model_path"] = "./saved_models"
-hypar["restore_model"] = "isnet.pth"
-hypar["interm_sup"] = False
-hypar["model_digit"] = "full"
-hypar["seed"] = 0
-hypar["cache_size"] = [1024, 1024]
-hypar["input_size"] = [1024, 1024]
-hypar["crop_size"] = [1024, 1024]
-hypar["model"] = ISNetDIS()
-
-net = build_model(hypar, device)
-
-def inference(image):
-    image_path = image
-    image_tensor, orig_size = load_image(image_path, hypar)
-    mask = predict(net, image_tensor, orig_size, hypar, device)
-    pil_mask = Image.fromarray(mask).convert('L')
-    im_rgb = Image.open(image).convert("RGB")
-    im_rgba = im_rgb.copy()
-    im_rgba.putalpha(pil_mask)
-    return [im_rgba, pil_mask]
-
-# Functions Added From Team
-def rotate_image(image, degrees):
-    img = Image.open(image).rotate(degrees)
-    return img
-
-def resize_image(image, width, height):
-    img = Image.open(image).resize((width, height))
-    return img
-
-def convert_to_grayscale(image):
-    img = Image.open(image).convert('L')
-    return img
-
-def adjust_brightness(image, brightness_factor):
-    img = Image.open(image)
-    enhancer = ImageEnhance.Brightness(img)
-    img_enhanced = enhancer.enhance(brightness_factor)
-    return img_enhanced
-
-# Custom CSS Added From Team
+# Custom CSS
 custom_css = """
 body {
     background-color: #f0f0f0;
@@ -205,62 +88,9 @@ body {
     border-radius: 12px;
     box-shadow: 0px 4px 16px rgba(0, 0, 0, 0.2);
 }
-button.lg {
-    background-color: #4CAF50;
-    color: white;
-    border: none;
-    padding: 10px 20px;
-    text-align: center;
-    text-decoration: none;
-    display: inline-block;
-    font-size: 16px;
-    margin: 4px 2px;
-    transition-duration: 0.4s;
-    cursor: pointer;
-    border-radius: 8px;
-}
-button.lg:hover {
-    background-color: #45a049;
-    color: white;
-}
 """
 
-# Used Some Codes From Yang's Chatbot
-with gr.Blocks(css=custom_css) as background_remover_interface:
-    gr.Markdown("<h1 style='text-align: center;'>🚩 Image Processor with Brightness Adjustment 🚩</h1>")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(label="Input Image", type='filepath')
-            rotate_button = gr.Button("Rotate Image")
-            resize_button = gr.Button("Resize Image")
-            grayscale_button = gr.Button("Convert to Grayscale")
-            brightness_slider = gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Adjust Brightness")
-            submit_button = gr.Button("Submit", variant="primary")
-            clear_button = gr.Button("Clear", variant="secondary")
-        with gr.Column():
-            output_image = gr.Image(label="Output Image")
-            mask_image = gr.Image(label="Mask")
-
-    # AI Generated: Use Gradio Blocks to organize the interface with buttons
-    rotate_button.click(rotate_image, inputs=[input_image, gr.Slider(minimum=0, maximum=360, step=1, value=90, label="Rotation Degrees")], outputs=output_image)
-    resize_button.click(resize_image, inputs=[input_image, gr.Number(value=512, label="Width"), gr.Number(value=512, label="Height")], outputs=output_image)
-    grayscale_button.click(convert_to_grayscale, inputs=input_image, outputs=output_image)
-    # input_image
-    brightness_slider.change(adjust_brightness, inputs=[input_image, brightness_slider], outputs=output_image)
-
-    submit_button.click(inference, inputs=input_image, outputs=[output_image, mask_image])
-
-    clear_button.click(lambda: (None, None, None), inputs=None, outputs=[input_image, output_image, mask_image])
-
-
-
-
-
-#####
-
-
-# --- Gradio Interfaces ---
-# AI Chatbot Interface
+# --- Gradio Interface ---
 with gr.Blocks(css=custom_css) as chatbot_interface:
     gr.Markdown("### AI Chatbot - Choose a personality and start chatting")
     personality = gr.Radio(choices=["Friendly", "Professional", "Humorous", "Empathetic"], value="Friendly", label="Personality")
@@ -278,12 +108,5 @@ with gr.Blocks(css=custom_css) as chatbot_interface:
     challenge_btn.click(generate_daily_challenge, inputs=history, outputs=chatbot)
     inspire_me_btn.click(generate_inspiration, inputs=history, outputs=chatbot)
 
-
-
-
-# Combine the two interfaces into a single app with tabs
-app = gr.TabbedInterface([chatbot_interface, background_remover_interface], ["AI Chatbot", "Background Remover"])
-
 # Launch the app
-#app.launch(share=True)  
-app.launch(server_name="0.0.0.0", server_port=8040, share=True, enable_queue=True)
+chatbot_interface.launch(share=True, enable_queue=True)