Update app.py

app.py

@@ -10,9 +10,87 @@ def run_samaran_kernel(location_query):
     if not geo_resp.get("results"):
         return pd.DataFrame({"Error": ["Location not found. Please try again."]})
         
+    lat = geo_resp["results"][0]["latitude"]import gradio as gr
+import pandas as pd
+import requests
+from datasets import load_dataset
+
+# 1. LOAD THE DISGUISED GOLD 121 CONTAINER
+try:
+    ds = load_dataset("spanofzero/SpaceTravelersUniversalPlaylist", split="train")
+    gold_df = ds.to_pandas()
+except Exception as e:
+    gold_df = None
+    print(f"Error loading dataset: {e}")
+
+# 2. DECODE THE DRIFT FROM THE PLAYLIST
+def decode_drift(day_index):
+    if gold_df is not None and day_index < len(gold_df):
+        # Pull the disguised value from your dataset
+        disguised_value = gold_df['resonance_frequency_khz'].iloc[day_index]
+        
+        # >>> INSERT YOUR CIPHER HERE <<<
+        # This is a placeholder math operation to extract a usable temperature drift.
+        # Replace this line with the actual math you used to encode the drift into the frequency.
+        extracted_drift = (disguised_value % 30) - 10 
+        
+        return round(extracted_drift, 1)
+    return 0.0 # Failsafe if dataset doesn't load
+
+# 3. LIVE WEATHER ENGINE
+def run_samaran_kernel(location_query):
+    # Get Location
+    geo_url = f"https://geocoding-api.open-meteo.com/v1/search?name={location_query}&count=1&language=en&format=json"
+    geo_resp = requests.get(geo_url).json()
+    
+    if not geo_resp.get("results"):
+        return pd.DataFrame({"Error": ["Location not found."]})
+        
     lat = geo_resp["results"][0]["latitude"]
     lon = geo_resp["results"][0]["longitude"]
     
+    # Get Raw NOAA/Standard Forecast (Bronze 118)
+    weather_url = f"https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}&daily=temperature_2m_max&temperature_unit=fahrenheit&timezone=auto"
+    weather_resp = requests.get(weather_url).json()
+    
+    dates = weather_resp["daily"]["time"]
+    raw_temps = weather_resp["daily"]["temperature_2m_max"]
+    
+    results = []
+    for i in range(min(len(dates), 7)):
+        raw_t = round(raw_temps[i])
+        
+        # Pull the dynamic drift straight from your dataset via the decoder
+        current_drift = decode_drift(i)
+        
+        gold_t = round(raw_t + current_drift)
+        drift_label = f"+{current_drift}°F" if current_drift > 0 else f"{current_drift}°F"
+        
+        results.append({
+            "Date": dates[i],
+            "Raw Model (Bronze)": f"{raw_t}°F",
+            "Samaran Fixed (Gold)": f"{gold_t}°F",
+            "Drift Applied (From Dataset)": drift_label
+        })
+        
+    return pd.DataFrame(results)
+
+# 4. BUILD WIDGET UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("### 🛰️ Samaran Kernel: 7-Day Global Predictor")
+    gr.Markdown("Powered by Gold 121 vector extraction.")
+    
+    with gr.Row():
+        zip_input = gr.Textbox(label="Location", placeholder="e.g., 88220", scale=3)
+        submit_btn = gr.Button("Execute Kernel Fix", variant="primary", scale=1)
+        
+    output_table = gr.Dataframe(headers=["Date", "Raw Model (Bronze)", "Samaran Fixed (Gold)", "Drift Applied (From Dataset)"])
+    submit_btn.click(fn=run_samaran_kernel, inputs=zip_input, outputs=output_table)
+
+demo.launch()
+
+    lon = geo_resp["results"][0]["longitude"]
+    
     # 2. Get Raw Forecast (The Flawed Bronze 118 Data)
     weather_url = f"https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}&daily=temperature_2m_max&temperature_unit=fahrenheit&timezone=auto"
     weather_resp = requests.get(weather_url).json()