Update app.py

app.py

@@ -1,67 +1,107 @@
-import time
-import pandas as pd
+# app.py
 import gradio as gr
-from selenium import webdriver
-from selenium.webdriver.chrome.service import Service
-from selenium.webdriver.common.by import By
-from selenium.webdriver.chrome.options import Options
-from webdriver_manager.chrome import ChromeDriverManager
-
-def scrape_snotel_data():
-    url = "https://www.weather.gov/wrh/timeseries?site=YCTIM"
+import requests
+import pandas as pd
+from datetime import datetime, timedelta
+import plotly.express as px
 
-    # Set up Selenium with headless Chrome
-    options = Options()
-    options.add_argument("--headless")
-    options.add_argument("--disable-gpu")
-    options.add_argument("--no-sandbox")
+def get_weather_data(station_id, hours=72):
+    """
+    Fetch weather data from NWS API
+    """
+    # NWS API requires a user agent header
+    headers = {
+        'User-Agent': '(Weather Data Fetcher, contact@yourdomain.com)',
+        'Accept': 'application/json'
+    }
     
-    service = Service(ChromeDriverManager().install())
-    driver = webdriver.Chrome(service=service, options=options)
-
+    # First, get the station details
+    station_url = f'https://api.weather.gov/stations/{station_id}'
     try:
-        driver.get(url)
-        time.sleep(5)  # Allow JavaScript to load (adjust time if necessary)
-
-        # Locate table
-        table = driver.find_element(By.TAG_NAME, "table")
-        if not table:
-            return pd.DataFrame([["Error", "No data table found on the page"]], columns=["Status", "Message"])
-
-        # Extract table headers
-        headers = [th.text.strip() for th in table.find_elements(By.TAG_NAME, "th")]
-
-        # Extract table rows
-        data = []
-        rows = table.find_elements(By.TAG_NAME, "tr")
-        for row in rows[1:]:  # Skip header row
-            cols = [td.text.strip() for td in row.find_elements(By.TAG_NAME, "td")]
-            if cols:
-                data.append(cols)
-
-        # Convert to DataFrame
-        df = pd.DataFrame(data, columns=headers)
+        response = requests.get(station_url, headers=headers)
+        response.raise_for_status()
+        station_data = response.json()
+        
+        # Get observations
+        observations_url = f'https://api.weather.gov/stations/{station_id}/observations'
+        params = {
+            'limit': hours,
+            'start': (datetime.utcnow() - timedelta(hours=hours)).isoformat() + 'Z'
+        }
+        
+        response = requests.get(observations_url, headers=headers, params=params)
+        response.raise_for_status()
         
-        return df
+        data = response.json()
+        
+        # Extract relevant information
+        records = []
+        for obs in data['features']:
+            props = obs['properties']
+            record = {
+                'timestamp': props['timestamp'],
+                'temperature': props['temperature']['value'],
+                'relative_humidity': props['relativeHumidity']['value'],
+                'wind_speed': props['windSpeed']['value'],
+                'wind_direction': props['windDirection']['value'],
+                'barometric_pressure': props['barometricPressure']['value']
+            }
+            records.append(record)
+            
+        df = pd.DataFrame(records)
+        df['timestamp'] = pd.to_datetime(df['timestamp'])
+        
+        # Convert temperature from Celsius to Fahrenheit
+        df['temperature'] = (df['temperature'] * 9/5) + 32
+        
+        return df, None
+        
+    except requests.exceptions.RequestException as e:
+        return None, f"Error fetching data: {str(e)}"
+
+def create_plot(df):
+    """
+    Create an interactive plot using plotly
+    """
+    fig = px.line(df, x='timestamp', y='temperature',
+                  title='Temperature Over Time',
+                  labels={'timestamp': 'Time', 'temperature': 'Temperature (°F)'})
+    return fig
 
-    except Exception as e:
-        return pd.DataFrame([["Error", str(e)]], columns=["Status", "Message"])
+def fetch_and_display(station_id, hours):
+    """
+    Main function to fetch data and create visualization
+    """
+    df, error = get_weather_data(station_id, hours)
     
-    finally:
-        driver.quit()
+    if error:
+        return None, error
+    
+    if df is not None and not df.empty:
+        fig = create_plot(df)
+        return fig, "Data fetched successfully!"
+    
+    return None, "No data available for the specified parameters."
 
-# Gradio interface
-def display_data():
-    df = scrape_snotel_data()
-    return df
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Weather Data Viewer")
+    
+    with gr.Row():
+        station_id = gr.Textbox(label="Station ID", value="YCTIM")
+        hours = gr.Slider(minimum=1, maximum=168, value=72, 
+                         label="Hours of Data", step=1)
+    
+    fetch_btn = gr.Button("Fetch Data")
+    plot = gr.Plot()
+    message = gr.Textbox(label="Status")
+    
+    fetch_btn.click(
+        fn=fetch_and_display,
+        inputs=[station_id, hours],
+        outputs=[plot, message]
+    )
 
-iface = gr.Interface(
-    fn=display_data,
-    inputs=None,
-    outputs="dataframe",
-    title="SNOTEL Data Viewer",
-    description="Scrapes and displays the latest SNOTEL data for YCTIM."
-)
+# Launch the app
+demo.launch()
 
-if __name__ == "__main__":
-    iface.launch()