Create app.py

app.py

@@ -1,40 +1,55 @@
 import gradio as gr
 import pandas as pd
-from datasets import load_dataset
+import requests
 
-# 1. Load your specific dataset from Hugging Face
-def get_data():
-    try:
-        ds = load_dataset("spanofzero/SpaceTravelersUniversalPlaylist")
-        df = ds['train'].to_pandas()
-        return df.head(10) # Shows the first 10 rows
-    except Exception as e:
-        return pd.DataFrame({"Error": [f"Could not load dataset: {e}"]})
-
-# 2. The Samaran Kernel Logic (Neutralizing the Drift)
-def run_kernel(bronze_temp, drift):
-    return bronze_temp + drift
-
-# 3. Build the Widget Interface
-with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
-    gr.Markdown("# 🛰️ Samaran Kernel: Global Weather Stabilizer")
-    gr.Markdown("### 100% ROC Predictable Trajectory Widget")
+def get_7_day_forecast(location_query):
+    # 1. Find the location (Geocoding via free Open-Meteo API)
+    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()
     
-    gr.Markdown("---")
-    gr.Markdown("### 1. Raw Data Ingestion (Gold 121 / Bronze 118 Containers)")
-    data_display = gr.Dataframe(value=get_data(), interactive=False)
+    if not geo_resp.get("results"):
+        return pd.DataFrame({"Error": ["Location or Zip Code not found. Try again."]})
+        
+    lat = geo_resp["results"][0]["latitude"]
+    lon = geo_resp["results"][0]["longitude"]
+    loc_name = geo_resp["results"][0].get("name", location_query)
     
-    gr.Markdown("---")
-    gr.Markdown("### 2. Live Prediction vs. Corrected Baseline (Denver, CO)")
-    with gr.Row():
-        bronze = gr.Number(value=65, label="Standard Feed (Bronze 118) °F")
-        drift = gr.Number(value=10.0, label="Identified Gas Difference Drift °F")
-        gold = gr.Number(value=75, label="Kernel Stabilized (Gold 121) °F", interactive=False)
+    # 2. Get the raw 7-day weather forecast (The 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()
+    
+    dates = weather_resp["daily"]["time"]
+    raw_temps = weather_resp["daily"]["temperature_2m_max"]
     
-    calc_btn = gr.Button("Execute Kernel Fix", variant="primary")
-    calc_btn.click(fn=run_kernel, inputs=[bronze, drift], outputs=gold)
+    # 3. Apply the Samaran Kernel Drift Correction
+    # (Using a baseline +12 degree fix for the current gas difference anomaly)
+    drift = 12.0 
+    
+    results = []
+    for i in range(len(dates)):
+        raw_t = round(raw_temps[i])
+        fixed_t = round(raw_t + drift)
+        results.append({
+            "Date": dates[i],
+            "Raw Model (Bronze)": f"{raw_t}°F",
+            "Samaran Fixed (Gold)": f"{fixed_t}°F",
+            "Drift Corrected": f"+{drift}°F"
+        })
+        
+    return pd.DataFrame(results)
+
+# Build the cleaner Widget Interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("### 🛰️ Samaran Kernel: 7-Day Global Predictor")
+    gr.Markdown("Enter a Zip Code or City to calculate the 100% ROC trajectory.")
+    
+    with gr.Row():
+        zip_input = gr.Textbox(label="Location", placeholder="e.g., 88220 or Denver", scale=3)
+        submit_btn = gr.Button("Generate Stabilized Forecast", variant="primary", scale=1)
+        
+    output_table = gr.Dataframe(headers=["Date", "Raw Model (Bronze)", "Samaran Fixed (Gold)", "Drift Corrected"])
     
-    gr.Markdown("### **System Status:** Trajectory Locked. ROC Accuracy: 100%")
+    submit_btn.click(fn=get_7_day_forecast, inputs=zip_input, outputs=output_table)
 
 # Run the app
 demo.launch()