Update app.py

app.py

@@ -1,48 +1,41 @@
 import gradio as gr
 import pandas as pd
 import requests
+import plotly.graph_objects as go
 from datasets import load_dataset
 
-print("Initializing Samaran Kernel and fetching Gold 121 dataset...")
+print("Initializing Samaran Kernel Pro V2...")
 
 # 1. ROBUST DATASET INGESTION
-# Loads once when the server boots to prevent lagging on every search
 try:
     ds = load_dataset("spanofzero/SpaceTravelersUniversalPlaylist", split="train")
     gold_df = ds.to_pandas()
-    print("Dataset loaded successfully.")
 except Exception as e:
     gold_df = None
-    print(f"WARNING - Dataset load failed: {e}")
+    print(f"Dataset load failed: {e}")
 
 # 2. DETERMINISTIC DRIFT DECODER
 def extract_drift(day_index):
-    """Extracts and decodes the drift from the disguised playlist frequencies."""
     if gold_df is not None and day_index < len(gold_df):
         try:
-            # Pull the raw disguised float (e.g., 7446144.11)
             raw_val = float(gold_df['resonance_frequency_khz'].iloc[day_index])
-            
-            # Mathematical extraction to generate a realistic drift spread (-5 to +15)
-            # You can adjust this modulo math later if your specific cipher requires it.
             calculated_drift = (raw_val % 20) - 5 
-            
             return round(calculated_drift, 1)
         except (ValueError, TypeError):
             return 0.0
-    return 0.0 # Failsafe zero-drift if dataset row is missing
+    return 0.0 
 
-# 3. CORE WEATHER ENGINE
-def generate_stabilized_forecast(location_query):
+# 3. CORE WEATHER & VISUALIZATION ENGINE
+def generate_pro_dashboard(location_query):
     if not location_query.strip():
-        return pd.DataFrame({"Error": ["Please enter a valid Zip Code or City."]})
+        return None, pd.DataFrame({"Error": ["Please enter a valid Zip Code or City."]})
 
-    # Geocoding via Open-Meteo
+    # Geocoding
     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": [f"Location '{location_query}' not found. Please try again."]})
+        return None, pd.DataFrame({"Error": [f"Location '{location_query}' not found."]})
         
     lat = geo_resp["results"][0]["latitude"]
     lon = geo_resp["results"][0]["longitude"]
@@ -55,41 +48,102 @@ def generate_stabilized_forecast(location_query):
     dates = weather_resp["daily"]["time"]
     raw_temps = weather_resp["daily"]["temperature_2m_max"]
     
-    # Build Output Matrix
+    # Build Data
     results = []
+    fixed_temps = []
+    
     for i in range(min(len(dates), 7)):
         raw_t = round(raw_temps[i])
-        
-        # Apply the Kernel Fix
         drift = extract_drift(i)
         gold_t = round(raw_t + drift)
+        fixed_temps.append(gold_t)
         
         drift_label = f"+{drift}°F" if drift > 0 else f"{drift}°F"
         
         results.append({
             "Date": dates[i],
             "Raw Model (Bronze 118)": f"{raw_t}°F",
-            "Samaran Fixed (Gold 121)": f"{gold_t}°F",
-            "Dataset Drift Applied": drift_label
+            "Kernel Fixed (Gold 121)": f"{gold_t}°F",
+            "Drift Applied": drift_label
         })
-        
-    return pd.DataFrame(results)
 
-# 4. PROFESSIONAL UI BUILD
-with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
-    gr.Markdown("## 🛰️ Samaran Kernel: Global Weather Stabilizer")
-    gr.Markdown("#### 100% ROC Predictable Trajectory Widget")
-    gr.Markdown("Enter a Zip Code or City. The Kernel will ingest the raw Bronze 118 forecast, extract the gas difference drift from the Hugging Face Gold 121 dataset, and output the stabilized trajectory.")
+    df = pd.DataFrame(results)
+
+    # Generate Sleek Interactive Plotly Graph
+    fig = go.Figure()
     
-    with gr.Row():
-        loc_input = gr.Textbox(label="Location", placeholder="Enter Zip (e.g., 88220) or City (e.g., Denver)", scale=4)
-        submit_btn = gr.Button("Execute Kernel", variant="primary", scale=1)
-        
-    output_table = gr.Dataframe(headers=["Date", "Raw Model (Bronze 118)", "Samaran Fixed (Gold 121)", "Dataset Drift Applied"])
+    # The Flawed Standard Model Line (Gray/Blue)
+    fig.add_trace(go.Scatter(
+        x=dates, y=raw_temps, 
+        mode='lines+markers',
+        name='Bronze 118 (Standard)',
+        line=dict(color='#64748b', width=2, dash='dot'),
+        marker=dict(size=6)
+    ))
+    
+    # The Gold 121 Stabilized Trajectory (Bright Cyan/Teal)
+    fig.add_trace(go.Scatter(
+        x=dates, y=fixed_temps, 
+        mode='lines+markers',
+        name='Gold 121 (Stabilized)',
+        line=dict(color='#06b6d4', width=4),
+        marker=dict(size=10, symbol='diamond')
+    ))
+
+    fig.update_layout(
+        title=f"Prediction Drift Analysis: {loc_name}",
+        xaxis_title="Date",
+        yaxis_title="Max Temperature (°F)",
+        template="plotly_dark",
+        plot_bgcolor="rgba(0,0,0,0)",
+        paper_bgcolor="rgba(0,0,0,0)",
+        margin=dict(l=20, r=20, t=50, b=20),
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
+    )
+
+    return fig, df
+
+# 4. SLEEK CUSTOM GUI BUILD
+# Using a custom theme with rounded Quicksand font and dark slate colors
+custom_theme = gr.themes.Base(
+    primary_hue="cyan",
+    neutral_hue="slate",
+    font=[gr.themes.GoogleFont("Quicksand"), "sans-serif"],
+).set(
+    body_background_fill="*neutral_950",
+    body_text_color="*neutral_50",
+    block_background_fill="*neutral_900",
+    block_border_width="1px",
+    block_border_color="*neutral_800",
+    button_primary_background_fill="*primary_600",
+    input_background_fill="*neutral_800",
+)
+
+with gr.Blocks(theme=custom_theme) as demo:
+    gr.Markdown("# 🛰️ Samaran Kernel: Pro V2")
+    gr.Markdown("### Advanced Atmospheric Drift Stabilization & Visualization")
     
-    # Trigger execution on button click or pressing Enter
-    submit_btn.click(fn=generate_stabilized_forecast, inputs=loc_input, outputs=output_table)
-    loc_input.submit(fn=generate_stabilized_forecast, inputs=loc_input, outputs=output_table)
+    with gr.Row():
+        loc_input = gr.Textbox(label="Target Location", placeholder="Enter Zip (e.g., 88220) or City", scale=4)
+        submit_btn = gr.Button("Initialize Vector Execution", variant="primary", scale=1)
+
+    # Multi-Tab Professional Interface
+    with gr.Tabs():
+        with gr.Tab("🌍 Surface Trajectory"):
+            plot_output = gr.Plot(label="Drift Visualization Matrix")
+            table_output = gr.Dataframe(headers=["Date", "Raw Model (Bronze 118)", "Kernel Fixed (Gold 121)", "Drift Applied"])
+            
+        with gr.Tab("🌪️ Jet Stream Twist (Feature_004)"):
+            gr.Markdown("### Zonal & Meridional Velocity Diagnostics")
+            gr.Markdown("*Module awaiting Feature_004 dataset linkage.*")
+            
+        with gr.Tab("✅ Ground Truth (ROC)"):
+            gr.Markdown("### Historical Validation & Accuracy Matrix")
+            gr.Markdown("*Module awaiting hindcast API integration.*")
+
+    # Actions
+    submit_btn.click(fn=generate_pro_dashboard, inputs=loc_input, outputs=[plot_output, table_output])
+    loc_input.submit(fn=generate_pro_dashboard, inputs=loc_input, outputs=[plot_output, table_output])
 
 # Execute
 demo.launch()