Update app.py

app.py

@@ -1,13 +1,8 @@
-import io
-import random
-from typing import List, Tuple
-
-import aiohttp
+import json
 import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
+from sentrifyai import api
 
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
+pn.extension(sizing_mode="stretch_width")
 
 ICON_URLS = {
     "brand-github": "https://github.com/holoviz/panel",
@@ -17,110 +12,57 @@ ICON_URLS = {
     "brand-discord": "https://discord.gg/AXRHnJU6sP",
 }
 
+async def classify_emotion(message: str):
+    emotions = api.Emotions()
+    try:
+        results = emotions.emotion(model_slug='Emotion-1.0', message=message)
+        return results
+    except Exception as e:
+        return {"error": str(e)}
 
-async def random_url(_):
-    pet = random.choice(["cat", "dog"])
-    api_url = f"https://api.the{pet}api.com/v1/images/search"
-    async with aiohttp.ClientSession() as session:
-        async with session.get(api_url) as resp:
-            return (await resp.json())[0]["url"]
-
-
-@pn.cache
-def load_processor_model(
-    processor_name: str, model_name: str
-) -> Tuple[CLIPProcessor, CLIPModel]:
-    processor = CLIPProcessor.from_pretrained(processor_name)
-    model = CLIPModel.from_pretrained(model_name)
-    return processor, model
-
-
-async def open_image_url(image_url: str) -> Image:
-    async with aiohttp.ClientSession() as session:
-        async with session.get(image_url) as resp:
-            return Image.open(io.BytesIO(await resp.read()))
-
-
-def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
-    processor, model = load_processor_model(
-        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
-    )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
-    )
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
-    return class_likelihoods[0]
-
-
-async def process_inputs(class_names: List[str], image_url: str):
-    """
-    High level function that takes in the user inputs and returns the
-    classification results as panel objects.
-    """
+def process_inputs(message: str):
     try:
         main.disabled = True
-        if not image_url:
-            yield "##### ⚠️ Provide an image URL"
-            return
-    
-        yield "##### ⚙ Fetching image and running model..."
-        try:
-            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)
+        
+        # Perform emotion classification
+        yield "##### ⚙️ Classifying emotions..."
+        results = yield from classify_emotion(message)
+        
+        # Display results
+        yield "##### 🎉 Emotion Classification Results:"
+        if "error" in results:
+            yield f"Error: {results['error']}"
+        else:
+            for emotion, score in results.items():
+                yield f"{emotion}: {score:.2f}"
     
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
-    
-        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",
+message_input = pn.widgets.TextInput(
+    name="Enter a message for emotion classification",
+    placeholder="Type your message here...",
+    sizing_mode="stretch_width"
 )
 
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
-)
+classify_button = pn.widgets.Button(name="Classify Emotion", button_type="primary")
+
+# define callback function for button click
+def on_button_click(event):
+    message = message_input.value
+    if message:
+        generator = process_inputs(message)
+        panel_content[:] = generator
+
+classify_button.on_click(on_button_click)
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
+# create main panel content
+panel_content = pn.Column(
+    "### 😊 Emotion Classification",
+    message_input,
+    classify_button,
 )
 
 # add footer
@@ -132,16 +74,14 @@ for icon, url in ICON_URLS.items():
 footer_row.append(pn.Spacer())
 
 # create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
+main = pn.Column(
+    panel_content,
     footer_row,
 )
 
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
+title = "Emotion Classification"
+pn.template.MaterialTemplate(
     title=title,
     main=main,
-    main_max_width="min(50%, 698px)",
     header_background="#F08080",
-).servable(title=title)
+).servable(title=title)