Update app.py

app.py

@@ -1,147 +1,115 @@
-import io
-import random
-from typing import List, Tuple
-
-import aiohttp
 import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
-
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
+import pandas as pd
+import numpy as np
+import hvplot.pandas
+import asyncio
+from datetime import datetime, timedelta
+
+# --- Configuration ---
+pn.extension('tabulator', sizing_mode="stretch_width")
+
+# --- Dummy Data Generation ---
+def generate_dummy_data(n_points=100):
+    """Generates a DataFrame with simulated stock data."""
+    start_time = datetime.now() - timedelta(minutes=n_points)
+    time_index = pd.to_datetime([start_time + timedelta(minutes=i) for i in range(n_points)])
+    price = 100 + np.random.randn(n_points).cumsum()
+    return pd.DataFrame({'Time': time_index, 'Price': price}).set_index('Time')
+
+def generate_ai_signal(current_price):
+    """Simulates an AI model generating a trading signal."""
+    # Simple logic: buy if price is 'low', sell if 'high', hold otherwise.
+    if current_price % 10 < 3:
+        return 'BUY', 'green'
+    elif current_price % 10 > 7:
+        return 'SELL', 'red'
+    return 'HOLD', 'orange'
+
+# --- Initial Data ---
+data = generate_dummy_data()
+current_signal, signal_color = generate_ai_signal(data['Price'].iloc[-1])
+
+# --- Dashboard Components ---
+
+# 1. User Controls
+symbol_input = pn.widgets.TextInput(name='Stock Symbol', value='AI_STOCK', width=150)
+update_interval = pn.widgets.IntSlider(name='Update Interval (s)', start=1, end=10, step=1, value=2, width=200)
+
+# 2. AI Signal Indicator
+signal_indicator = pn.indicators.Number(
+    name='AI Signal',
+    value=0, # Will be updated by text
+    format=f'{current_signal}',
+    font_size='36pt',
+    colors=[(999, signal_color)] # A single color based on the signal
+)
 
-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",
+# 3. Performance Metrics
+metrics = {
+    'Metric': ['Win Rate', 'Profit Factor', 'Sharpe Ratio'],
+    'Value': ['62%', '1.85', '1.2'] # Dummy values
 }
-
-
-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.
-    """
-    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)
-    
-        # 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",
+metrics_table = pn.widgets.Tabulator(pd.DataFrame(metrics), disabled=True, selectable=False)
+
+# 4. Price Chart
+price_chart = data.hvplot.line(
+    y='Price',
+    line_width=3,
+    height=400,
+    title="Live Stock Price",
+    xlabel="Time",
+    ylabel="Price",
+    responsive=True
+).opts(
+    yformatter='%.2f'
 )
 
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
+# --- Layout ---
+sidebar = pn.Column(
+    "## ⚙️ Controls",
+    symbol_input,
+    update_interval,
+    "## 🤖 AI Analysis",
+    signal_indicator,
+    "## 📈 Performance",
+    metrics_table,
+    width=300
 )
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
+main_content = pn.Column(
+    price_chart
 )
 
-# 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())
-
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
+dashboard_layout = pn.template.FastListTemplate(
+    site="AI Trading Dashboard",
+    title=f"Live Analysis for {symbol_input.value}",
+    sidebar=[sidebar],
+    main=[main_content]
 )
 
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+# --- Interactivity & Live Updates ---
+stream = hvplot.stream.Buffer(data, index=False, length=100)
+price_chart.update(stream)
+
+async def update_data():
+    """Callback to simulate live data feed and update dashboard elements."""
+    while True:
+        await asyncio.sleep(update_interval.value)
+        
+        # 1. Simulate new data point
+        last_time = data.index[-1]
+        new_time = last_time + timedelta(seconds=10) # Advance time
+        new_price = data['Price'].iloc[-1] + np.random.randn() * 0.5
+        new_data_point = pd.DataFrame([{'Time': new_time, 'Price': new_price}]).set_index('Time')
+
+        # 2. Update chart stream
+        stream.send(new_data_point)
+        
+        # 3. Update AI Signal
+        new_signal, new_color = generate_ai_signal(new_price)
+        signal_indicator.format = f'{new_signal}' # Update text
+        signal_indicator.colors = [(999, new_color)] # Update color
+
+# --- Add dashboard to the document and start the update loop ---
+dashboard_layout.servable()
+pn.state.onload(update_data)