Update app.py

app.py

@@ -1,122 +1,26 @@
-import io
-import random
-from typing import List, Tuple
-
-import aiohttp
-import panel as pn
-from PIL import Image
-# from transformers import CLIPModel, CLIPProcessor
+import gradio as gr
+from transformers import pipeline
+import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
-
-ICON_URLS = {
-    "brand-github": "https://github.com/holoviz/panel",
-    "brand-twitter": "https://twitter.com/Panel_Org",
-    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
-    "message-circle": "https://discourse.holoviz.org/",
-    "brand-discord": "https://discord.gg/AXRHnJU6sP",
-}
-
-
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-base", torch_dtype=torch.bfloat16)
 
-def load_tokenizer_model():
-    tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-base", trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-base", torch_dtype=torch.bfloat16)
-    return tokenizer,model
+print()
 
 
+# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-async def process_inputs(class_names: List[str], user_text: str):
-    """
-    High level function that takes in the user inputs and returns the
-    classification results as panel objects.
-    """
-    try:
-        main.disabled = True
-        if not user_text:
-            yield "##### ⚠️ Provide some user text URL"
-            return
-    
-        yield "##### ⚙ Fetching and running model..."
-        try:
-            inputs = tokenizer("The world is", return_tensors="pt")
-            sample = model.generate(**inputs, max_length=128)
-
-            # pil_img = await open_image_url(image_url)
-            # img = pn.pane.Image(pil_img, height=400, align="center")
-        except Exception as e:
-            yield f"##### 😔 Something went wrong, please try a different URL!"
-            return
-    
-        # class_items = class_names.split(",")
-        # class_likelihoods = get_similarity_scores(class_items, pil_img)
+def gentext(user_input="The world is"):
+    inputs = tokenizer(user_input, return_tensors="pt")
+    sample = model.generate(**inputs, max_length=128)
     
-        # build the results column
-            
-        results = pn.Column("##### 🎉 Here are the results!", tokenizer.decode(sample[0])))
-        
-        # for class_item, class_likelihood in zip(class_items, class_likelihoods):
-        #     row_label = pn.widgets.StaticText(
-        #         name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
-        #     )
-        #     row_bar = pn.indicators.Progress(
-        #         value=int(class_likelihood * 100),
-        #         sizing_mode="stretch_width",
-        #         bar_color="secondary",
-        #         margin=(0, 10),
-        #         design=pn.theme.Material,
-        #     )
-        #     results.append(pn.Column(row_label, row_bar))
-        yield results
-    finally:
-        main.disabled = False
-
-
-# create widgets
-randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
-
-image_url = pn.widgets.TextInput(
-    name="Image URL to classify",
-    value=pn.bind(random_url, randomize_url),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
-)
-
-input_widgets = pn.Column(
-    "##### Add some text and do something",
-    pn.Row(image_url, randomize_url),
-    class_names,
-)
-
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
-)
-
-# add footer
-footer_row = pn.Row(pn.Spacer(), align="center")
-for icon, url in ICON_URLS.items():
-    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
-    href_button.js_on_click(code=f"window.open('{url}')")
-    footer_row.append(href_button)
-footer_row.append(pn.Spacer())
+    return {"output": tokenizer.decode(sample[0])}
 
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
-)
 
-title = "Xgen input panel"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+gr.Interface(
+    gentext,
+    inputs=gr.inputs.Text(label="Some prompt", type="input"),
+    outputs=gr.outputs.Label(num_top_classes=2),
+    title="Some prompt",
+).launch()