update everything

app.py

 # app.py
 
+# --- Imports ---
 import pandas as pd
 import traceback
-from flask import Flask, render_template, url_for
+from flask import Flask, render_template, request
 from prophet import Prophet
 from prophet.diagnostics import cross_validation, performance_metrics
 from datetime import datetime
-import math
-import os # For environment variables (recommended for API keys)
+import json
 
 # --- Import data fetching functions ---
 try:
-    from data import (fetch_crypto_price,
-                      fetch_gemini_historical_data, fetch_current_fear_greed,
-                      fetch_historical_fear_greed, FNG_LIB_AVAILABLE) # Import flag too
+    from data import (fetch_crypto_price, fetch_gemini_historical_data,
+                      fetch_current_fear_greed, fetch_historical_fear_greed,
+                      fetch_reddit_sentiment)
     print("--- Successfully imported functions from data.py ---")
 except ImportError as e:
     print(f"!!! ERROR importing from data.py: {e} !!!")
-    # Define dummy functions if import fails
     def fetch_crypto_price(*args, **kwargs): return None
     def fetch_gemini_historical_data(*args, **kwargs): return None
     def fetch_current_fear_greed(*args, **kwargs): return None
     def fetch_historical_fear_greed(*args, **kwargs): return None
-    FNG_LIB_AVAILABLE = False
+    def fetch_reddit_sentiment(*args, **kwargs): return None
 
 # --- Flask App Initialization ---
-app = Flask(__name__) # Looks for templates/ and static/ folders
+app = Flask(__name__)
 
 # --- Main Route ---
 @app.route('/')
@@ -34,8 +33,8 @@ def index():
     print(f"\n--- Request received at {start_time.strftime('%Y-%m-%d %H:%M:%S')} ---")
 
     # --- Configuration ---
-    symbol = 'BTCUSD'
-    coin_gecko_id = 'bitcoin'
+    symbol = request.args.get('symbol', 'BTCUSD')  # Support dropdown selection
+    coin_gecko_id = {'BTCUSD': 'bitcoin', 'ETHUSD': 'ethereum', 'SOLUSD': 'solana'}.get(symbol, 'bitcoin')
     timeframe = '1hr'
     min_data_points = 25
 
@@ -48,7 +47,7 @@ def index():
     df_merged = None
     df_fng = None
     fng_available = False
-    used_current_fng_for_future = False
+    reddit_sentiment = None
     model = None
     forecast = None
     future = None
@@ -62,39 +61,53 @@ def index():
     historical_accuracy_percent = None
     data_confidence_score = 0
     prediction_interval_penalty = 0
+    used_current_fng_for_future = False
     plot_data = None
     error_message = None
+    sources = []  # List to store sources for frontend
 
     # --- 1. FETCH DATA ---
     try:
         print("--- Fetching Data ---")
-        # Fetch in parallel might be faster but adds complexity, sequential for now
         historical_data = fetch_gemini_historical_data(symbol, timeframe=timeframe)
+        if historical_data:
+            sources.append({"name": "Gemini API", "type": "Price Data", "reliability": "High"})
         current_price = fetch_crypto_price(coin_id=coin_gecko_id)
-        if FNG_LIB_AVAILABLE:
         current_fng_data = fetch_current_fear_greed()
-            current_fng_value = current_fng_data.get('value') if isinstance(current_fng_data, dict) else None
-        print(f"Fetched: Current Price={current_price}, Current F&G={current_fng_value}")
+        current_fng_value = current_fng_data.get('value') if current_fng_data else None
+        if current_fng_value:
+            sources.append({"name": "Fear & Greed Index", "type": "Market Sentiment", "reliability": "Medium"})
+        reddit_sentiment = fetch_reddit_sentiment(subreddit="CryptoCurrency", query=coin_gecko_id, timeframe_hours=24)
+        if reddit_sentiment:
+            sources.append({"name": "Reddit (r/CryptoCurrency)", "type": "Social Media", "reliability": "Medium"})
+        print(f"Fetched: Current Price={current_price}, Current F&G={current_fng_value}, Reddit Sentiment={reddit_sentiment}")
     except Exception as fetch_e:
         error_message = "Error during initial data fetching."
         print(f"!!! {error_message}: {fetch_e} !!!")
-        traceback.print_exc()
 
     # --- 2. PROCESS PRICE/VOLUME DATA ---
-    if historical_data and isinstance(historical_data, list) and len(historical_data) >= min_data_points:
+    if historical_data and len(historical_data) >= min_data_points:
         try:
             print("--- Processing Price/Volume data ---")
             df = pd.DataFrame(historical_data, columns=['timestamp_ms', 'y', 'volume'])
             df['ds'] = pd.to_datetime(df['timestamp_ms'], unit='ms')
-            df.dropna(subset=['ds', 'y'], inplace=True) # Essential columns must be valid
+            df.dropna(subset=['ds', 'y'], inplace=True)
             if df.empty:
                 raise ValueError("No valid rows remaining after dropping NaNs in 'ds' or 'y'.")
-            df['volume'] = pd.to_numeric(df['volume'], errors='coerce').fillna(0) # Clean volume early
             df_prophet = df[['ds', 'y', 'volume']].copy()
 
-            data_confidence_score = 50
-            if len(df_prophet) > 100: data_confidence_score += 10
-            print(f"Data Confidence: Base score {data_confidence_score} after price/vol check.")
+            # Calculate base confidence score
+            data_confidence_score = 50  # Base for having minimum data
+            data_confidence_score += 5 * len(sources)  # +5 per source
+            if len(df_prophet) > 100:
+                data_confidence_score += 10
+            # Add reliability bonus
+            for source in sources:
+                if source['reliability'] == "High":
+                    data_confidence_score += 10
+                elif source['reliability'] == "Medium":
+                    data_confidence_score += 5
+            print(f"Data Confidence: Base score {data_confidence_score} after price/vol and sources check.")
         except Exception as e:
             error_message = "Error processing historical price/volume data."
             print(f"!!! {error_message}: {e} !!!")
@@ -102,21 +115,25 @@ def index():
             df_prophet = None
             data_confidence_score = 0
     else:
-        # Handle insufficient or failed price data fetch
-        if historical_data is None: error_message = f"Failed to fetch historical data from API for {symbol}/{timeframe}."
-        elif not isinstance(historical_data, list): error_message = f"Historical data received in unexpected format: {type(historical_data)}"
-        else: error_message = f"Insufficient historical data ({len(historical_data)} points) for prediction."
+        if historical_data is None:
+            error_message = f"Failed to fetch historical data from API for {symbol}/{timeframe}."
+        else:
+            error_message = f"Insufficient historical data ({len(historical_data)} points) for prediction."
         print(f"!!! {error_message} Confidence Score: {data_confidence_score} !!!")
         df_prophet = None
-        # Render early if core data is missing/insufficient
         processing_time = (datetime.now() - start_time).total_seconds()
         print(f"--- Request processing finished early in {processing_time:.2f} seconds due to data issue. Rendering template... ---")
-        return render_template('dashboard.html', symbol=symbol, current_price=current_price, current_fng_value=current_fng_value,
-                               error_message=error_message, data_confidence_score=data_confidence_score, plot_data=None,
-                               processing_time=f"{processing_time:.2f} sec", last_updated=datetime.now().strftime("%Y-%m-%d %H:%M:%S %Z"))
+        return render_template('dashboard.html',
+                               symbol=symbol,
+                               error_message=error_message,
+                               data_confidence_score=data_confidence_score,
+                               plot_data=None,
+                               sources=sources,
+                               processing_time=f"{processing_time:.2f} sec",
+                               last_updated=datetime.now().strftime("%Y-%m-%d %H:%M:%S %Z"))
 
     # --- 2b. FETCH & ATTEMPT MERGE FEAR/GREED DATA ---
-    if df_prophet is not None and FNG_LIB_AVAILABLE:
+    if df_prophet is not None:
         try:
             start_date = df_prophet['ds'].min()
             end_date = df_prophet['ds'].max()
@@ -128,134 +145,116 @@ def index():
                 df_prophet_with_date['ds_date'] = df_prophet_with_date['ds'].dt.normalize()
                 temp_merged = pd.merge(df_prophet_with_date, df_fng, on='ds_date', how='left')
                 temp_merged['fear_greed'] = temp_merged['fear_greed'].ffill().bfill()
-                # FIX for Pandas FutureWarning: Use direct assignment instead of inplace=True on potentially chained access
-                temp_merged['fear_greed'] = temp_merged['fear_greed'].fillna(50) # Final fallback
+                if temp_merged['fear_greed'].isnull().any():
+                    print("!!! Warning: Could not fill all missing Fear/Greed values. Filling remaining with 50. !!!")
+                    temp_merged['fear_greed'].fillna(50, inplace=True)
                 df_merged = temp_merged[['ds', 'y', 'volume', 'fear_greed']].copy()
                 fng_available = True
-                data_confidence_score += 25
+                data_confidence_score += 15
                 print(f"Data Confidence: Added F&G bonus. Score = {data_confidence_score}")
-                print(f"Successfully merged F&G data. Fitting DataFrame has columns: {list(df_merged.columns)}")
             else:
                 print("--- Proceeding without Fear/Greed data (fetch failed or no data). ---")
                 df_merged = df_prophet.copy()
                 fng_available = False
-                 print(f"Data Confidence: No F&G bonus applied. Score = {data_confidence_score}")
         except Exception as e:
-            if not error_message: error_message = "Error merging Fear/Greed data."
+            if not error_message:
+                error_message = "Error merging Fear/Greed data."
             print(f"!!! {error_message}: {e} !!!")
             traceback.print_exc()
             df_merged = df_prophet.copy()
             fng_available = False
-            print(f"Data Confidence: F&G merge failed. Score = {data_confidence_score}")
-    elif df_prophet is not None: # FNG lib not available
-        print("--- Skipping F&G merge: Library not available ---")
+
+    # --- 2c. MERGE REDDIT SENTIMENT ---
+    if df_prophet is not None and reddit_sentiment and 'average_score' in reddit_sentiment:
+        try:
+            df_merged['reddit_sentiment'] = reddit_sentiment['average_score']
+            # Add precision bonus based on sentiment variance
+            if 'variance' in reddit_sentiment:
+                variance = reddit_sentiment['variance']
+                if variance < 0.1:  # Low variance = precise
+                    data_confidence_score += 10
+                elif variance < 0.3:
+                    data_confidence_score += 5
+                print(f"Data Confidence: Added Reddit sentiment precision bonus (variance={variance}). Score = {data_confidence_score}")
+        except Exception as e:
+            print(f"!!! Error merging Reddit sentiment: {e} !!!")
+            if df_merged is None:
                 df_merged = df_prophet.copy()
-        fng_available = False
 
     # --- 3. MODEL TRAINING & PREDICTION ---
     if df_merged is not None:
         try:
             print("--- Instantiating Prophet model ---")
-            model = Prophet(interval_width=0.95) # 95% prediction interval
-            volume_regressor_added = False # <<< FIX: Initialize flag
-            fng_regressor_added = False    # <<< FIX: Initialize flag
-            print("--- Adding Regressors ---")
-
-            # Add 'volume' regressor if valid
-            if 'volume' in df_merged.columns and df_merged['volume'].notna().any():
+            model = Prophet(interval_width=0.95)
+            if 'volume' in df_merged.columns and not df_merged['volume'].isnull().all():
+                df_merged['volume'] = pd.to_numeric(df_merged['volume'], errors='coerce').fillna(0)
                 model.add_regressor('volume')
-                 volume_regressor_added = True # <<< FIX: Set flag
-                 print("Added 'volume' regressor.")
-            else: print("--- Skipping 'volume' regressor (column missing or all NaN) ---")
-
-            # Add 'fear_greed' regressor if valid and available
-            if fng_available and 'fear_greed' in df_merged.columns and df_merged['fear_greed'].notna().any():
+            if fng_available and 'fear_greed' in df_merged.columns and not df_merged['fear_greed'].isnull().all():
+                df_merged['fear_greed'] = pd.to_numeric(df_merged['fear_greed'], errors='coerce').fillna(50)
                 model.add_regressor('fear_greed')
-                 fng_regressor_added = True # <<< FIX: Set flag
-                 print("Added 'fear_greed' regressor.")
-            else:
-                 fng_available = False # Ensure state consistency
-                 print("Fitting model WITHOUT 'fear_greed' regressor (not available or all NaN).")
-
-            # Final check on data points before fitting
-            df_merged.dropna(subset=['y'], inplace=True) # Drop rows if 'y' became NaN
+            if 'reddit_sentiment' in df_merged.columns and not df_merged['reddit_sentiment'].isnull().all():
+                df_merged['reddit_sentiment'] = pd.to_numeric(df_merged['reddit_sentiment'], errors='coerce').fillna(0)
+                model.add_regressor('reddit_sentiment')
+
+            initial_rows = len(df_merged)
+            df_merged.dropna(subset=['y'], inplace=True)
+            if len(df_merged) < initial_rows:
+                print(f"--- Warning: Dropped {initial_rows - len(df_merged)} rows due to NaN in 'y' before fitting. ---")
             if len(df_merged) < min_data_points:
-                 raise ValueError(f"Insufficient data ({len(df_merged)} rows) remaining for model fitting after processing.")
+                raise ValueError(f"Insufficient data ({len(df_merged)} rows) remaining for model fitting after processing regressors.")
             
-            print(f"--- Fitting model on DataFrame with columns: {list(df_merged.columns)} ({len(df_merged)} rows) ---")
+            print(f"Fitting model on DataFrame with columns: {list(df_merged.columns)} ({len(df_merged)} rows)")
             model.fit(df_merged)
 
-            print("--- Making future dataframe... ---")
+            print("Making future dataframe...")
             future = model.make_future_dataframe(periods=24, freq='h')
 
-            print("--- Populating future regressor values... ---")
-            # <<< FIX: Use flags instead of checking model.regressors >>>
-            if volume_regressor_added:
-                last_volume = df_merged['volume'].iloc[-1] if not df_merged['volume'].empty else 0
-                future['volume'] = last_volume
-                print(f"   - Set future 'volume' to: {last_volume}")
-
-            # <<< FIX: Use flags instead of checking model.regressors >>>
-            if fng_regressor_added:
-                if current_fng_value is not None and pd.notna(current_fng_value):
+            if 'volume' in model.regressors:
+                future['volume'] = df_merged['volume'].iloc[-1] if not df_merged['volume'].empty else 0
+            if 'fear_greed' in model.regressors:
+                if current_fng_value is not None:
                     future['fear_greed'] = float(current_fng_value)
                     used_current_fng_for_future = True
-                    print(f"   - Set future 'fear_greed' to current value: {current_fng_value}.")
-                elif 'fear_greed' in df_merged.columns and not df_merged['fear_greed'].empty:
-                    last_hist_fng = df_merged['fear_greed'].iloc[-1]
-                    future['fear_greed'] = last_hist_fng
-                    print(f"   - Warning: Using last historical value ({last_hist_fng}) for future 'fear_greed'.")
-                else:
-                    future['fear_greed'] = 50
-                    print("   - Warning: Using 50 for future 'fear_greed'.")
-
-                if used_current_fng_for_future:
                     data_confidence_score += 5
-                    print(f"Data Confidence: Added Current F&G bonus. Score = {data_confidence_score}")
-                else: print("Data Confidence: No Current F&G bonus (used fallback).")
+                else:
+                    future['fear_greed'] = df_merged['fear_greed'].iloc[-1] if not df_merged['fear_greed'].empty else 50
+            if 'reddit_sentiment' in model.regressors:
+                future['reddit_sentiment'] = reddit_sentiment['average_score'] if reddit_sentiment and 'average_score' in reddit_sentiment else 0
 
-            print("--- Predicting future prices... ---")
+            print("Predicting...")
             forecast = model.predict(future)
-            print("--- Prediction complete. ---")
 
-            # --- Calculate Prediction Interval Penalty ---
-            if forecast is not None and len(forecast) >= len(df_merged) + 24: # Check full forecast length
+            # Prediction Interval Penalty
+            if forecast is not None and len(forecast) >= 24:
                 pred_24h_row = forecast.iloc[-1]
                 yhat = pred_24h_row.get('yhat')
                 yhat_lower = pred_24h_row.get('yhat_lower')
                 yhat_upper = pred_24h_row.get('yhat_upper')
-
                 if pd.notna(yhat) and yhat != 0 and pd.notna(yhat_lower) and pd.notna(yhat_upper):
                     interval_width = yhat_upper - yhat_lower
                     relative_width = abs(interval_width / yhat)
-                    print(f"Prediction Interval (24h): Width={interval_width:.2f}, Relative Width={relative_width:.3f}")
-                    if relative_width > 0.20: prediction_interval_penalty = 20
-                    elif relative_width > 0.10: prediction_interval_penalty = 10
-                    elif relative_width > 0.05: prediction_interval_penalty = 5
-                    else: prediction_interval_penalty = 0
+                    if relative_width > 0.20:
+                        prediction_interval_penalty = 20
+                    elif relative_width > 0.10:
+                        prediction_interval_penalty = 10
+                    elif relative_width > 0.05:
+                        prediction_interval_penalty = 5
                     data_confidence_score -= prediction_interval_penalty
                     print(f"Data Confidence: Applied Interval Penalty: -{prediction_interval_penalty}. Score = {data_confidence_score}")
-                else: print("Data Confidence: Could not calculate interval penalty (missing forecast/interval values).")
 
-            # --- Extract specific point predictions ---
+            # Extract predictions
             if forecast is not None:
-                hist_len = len(df_merged)
-                # Check length before accessing iloc
-                if len(forecast) > hist_len: predicted_price_1h = forecast.iloc[hist_len].get('yhat')
-                if len(forecast) > hist_len + 11: predicted_price_12h = forecast.iloc[hist_len + 11].get('yhat')
-                if len(forecast) > hist_len: predicted_price_24h = forecast.iloc[-1].get('yhat') # Last row is 24h forecast
-
-                if predicted_price_1h is None or predicted_price_12h is None or predicted_price_24h is None:
-                     if not error_message: error_message = "Prediction generated incomplete forecast."
-                     print(f"!!! {error_message} Forecast length: {len(forecast)} !!!")
-            else: predicted_price_1h = predicted_price_12h = predicted_price_24h = None
-
+                predicted_price_1h = forecast.iloc[-24].get('yhat') if len(forecast) >= 24 else None
+                predicted_price_12h = forecast.iloc[-13].get('yhat') if len(forecast) >= 13 else None
+                predicted_price_24h = forecast.iloc[-1].get('yhat') if len(forecast) >= 1 else None
         except Exception as e:
-            if not error_message: error_message = "Prediction model fitting or forecasting failed."
+            if not error_message:
+                error_message = "Prediction model fitting or forecasting failed."
             print(f"!!! {error_message}: {e} !!!")
             traceback.print_exc()
             predicted_price_1h = predicted_price_12h = predicted_price_24h = None
-            forecast = None; model = None
+            forecast = None
+            model = None
             data_confidence_score = max(0, data_confidence_score - 30)
 
     # --- Clamp final confidence score ---
@@ -271,45 +270,36 @@ def index():
             print(f"\n--- Running Prophet cross-validation (initial='{initial_train_period}', period='{period_between_forecasts}', horizon='{forecast_horizon}') ---")
             df_cv = cross_validation(model, initial=initial_train_period, period=period_between_forecasts, horizon=forecast_horizon, parallel="processes", disable_tqdm=True)
             df_p = performance_metrics(df_cv)
-            print("\nCross-Validation Performance Metrics (Sample):")
-            print(df_p[['horizon', 'mape', 'mae', 'rmse']].head())
             avg_mape = df_p['mape'].mean()
             avg_mae = df_p['mae'].mean()
-            if pd.notna(avg_mape) and pd.notna(avg_mae): accuracy_metrics = {'mape': avg_mape * 100, 'mae': avg_mae}
-            else: accuracy_metrics = None
+            if pd.notna(avg_mape) and pd.notna(avg_mae):
+                accuracy_metrics = {'mape': avg_mape * 100, 'mae': avg_mae}
             if pd.notna(avg_mape):
                 clamped_mape = max(0, min(avg_mape, 1.0))
                 historical_accuracy_percent = (1 - clamped_mape) * 100
-                 print(f"Calculated Historical Accuracy: {historical_accuracy_percent:.2f}% (based on Avg MAPE: {avg_mape:.4f})")
-            else:
-                 print("--- Could not calculate historical accuracy (MAPE unavailable) ---")
-                 historical_accuracy_percent = None
         except Exception as cv_e:
             print(f"!!! Cross-validation failed: {cv_e} !!!")
             traceback.print_exc()
-            if not error_message: error_message = "Accuracy cross-validation failed."
-            accuracy_metrics = None; historical_accuracy_percent = None
-    elif model is not None:
-        print(f"--- Skipping cross-validation due to insufficient data ({len(df_merged)} rows) ---")
-        if not error_message: error_message = "Not enough historical data for accuracy test."
-        accuracy_metrics = None; historical_accuracy_percent = None
+            if not error_message:
+                error_message = "Accuracy cross-validation failed."
+            accuracy_metrics = None
+            historical_accuracy_percent = None
 
     # --- 4. CALCULATE PERCENTAGE CHANGES ---
     print("--- Calculating percentage changes ---")
     if current_price is not None and current_price != 0:
         try:
-            if pd.notna(predicted_price_1h): percentage_change_1h = ((predicted_price_1h - current_price) / current_price) * 100
-            if pd.notna(predicted_price_12h): percentage_change_12h = ((predicted_price_12h - current_price) / current_price) * 100
-            if pd.notna(predicted_price_24h): percentage_change_24h = ((predicted_price_24h - current_price) / current_price) * 100
+            if predicted_price_1h is not None and pd.notna(predicted_price_1h):
+                percentage_change_1h = ((predicted_price_1h - current_price) / current_price) * 100
+            if predicted_price_12h is not None and pd.notna(predicted_price_12h):
+                percentage_change_12h = ((predicted_price_12h - current_price) / current_price) * 100
+            if predicted_price_24h is not None and pd.notna(predicted_price_24h):
+                percentage_change_24h = ((predicted_price_24h - current_price) / current_price) * 100
         except Exception as e:
             print(f"!!! Error calculating percentage changes: {e} !!!")
-            if not error_message: error_message = "Error calculating prediction changes."
             percentage_change_1h = percentage_change_12h = percentage_change_24h = None
-    elif current_price == 0: print("Warning: Current price is 0, cannot calculate percentage change.")
-    else: print("Warning: Current price not available, cannot calculate percentage change.")
 
     # --- 5. PREPARE DATA FOR PLOTTING ---
-    plot_data = None
     if df_merged is not None and forecast is not None:
         try:
             print("--- Preparing data for plotting ---")
@@ -318,12 +308,14 @@ def index():
             if len(forecast) > len(df_merged):
                 future_plot_points = forecast.iloc[len(df_merged):][['ds', 'yhat', 'yhat_lower', 'yhat_upper']].copy()
             else:
-                 print("--- Warning: Forecast length not greater than merged data length. No future points to plot. ---")
                 future_plot_points = pd.DataFrame(columns=['ds', 'yhat', 'yhat_lower', 'yhat_upper'])
 
             hist_dates = history_to_plot['ds']
             future_dates = future_plot_points['ds']
-            all_dates_dt = pd.concat([hist_dates, future_dates], ignore_index=True).sort_values() if not future_dates.empty else hist_dates.sort_values()
+            if not future_dates.empty:
+                all_dates_dt = pd.concat([pd.Series(hist_dates), pd.Series(future_dates)], ignore_index=True).sort_values()
+            else:
+                all_dates_dt = hist_dates.sort_values()
             all_dates_str = all_dates_dt.dt.strftime('%Y-%m-%dT%H:%M:%S').unique().tolist()
 
             hist_values_dict = history_to_plot.set_index(history_to_plot['ds'].dt.strftime('%Y-%m-%dT%H:%M:%S'))['y']
@@ -335,9 +327,7 @@ def index():
             pred_upper = [pred_values_dict.get(dt, {}).get('yhat_upper') for dt in all_dates_str]
 
             def round_if_not_none(val, digits=2):
-                if val is None or (isinstance(val, (float, int)) and math.isnan(float(val))): return None
-                try: return round(float(val), digits)
-                except (ValueError, TypeError): return None
+                return round(val, digits) if pd.notna(val) else None
 
             plot_data = {
                 "labels": all_dates_str,
@@ -346,40 +336,39 @@ def index():
                 "pred_lower": [round_if_not_none(v) for v in pred_lower],
                 "pred_upper": [round_if_not_none(v) for v in pred_upper]
             }
-            print(f"--- Prepared plot_data with {len(all_dates_str)} labels. ---")
         except Exception as plot_e:
             print(f"!!! Error preparing plot data: {plot_e} !!!")
             traceback.print_exc()
             plot_data = None
-    else:
-        print("--- Skipping plot data preparation (missing model data or forecast) ---")
-        plot_data = None
 
-    # --- 6. RENDER TEMPLATE ---
+    # --- 6. FORMAT VARIABLES FOR DISPLAY ---
+    current_price_display = "${:.2f}".format(current_price) if current_price is not None else "N/A"
+    predicted_price_1h_display = "${:.2f}".format(predicted_price_1h) if predicted_price_1h is not None else "N/A"
+    predicted_price_12h_display = "${:.2f}".format(predicted_price_12h) if predicted_price_12h is not None else "N/A"
+    predicted_price_24h_display = "${:.2f}".format(predicted_price_24h) if predicted_price_24h is not None else "N/A"
+    percentage_change_1h_display = "+{:.2f}%".format(percentage_change_1h) if percentage_change_1h is not None and percentage_change_1h > 0 else ("{:.2f}%".format(percentage_change_1h) if percentage_change_1h is not None else "N/A")
+    percentage_change_12h_display = "+{:.2f}%".format(percentage_change_12h) if percentage_change_12h is not None and percentage_change_12h > 0 else ("{:.2f}%".format(percentage_change_12h) if percentage_change_12h is not None else "N/A")
+    percentage_change_24h_display = "+{:.2f}%".format(percentage_change_24h) if percentage_change_24h is not None and percentage_change_24h > 0 else ("{:.2f}%".format(percentage_change_24h) if percentage_change_24h is not None else "N/A")
+
+    # --- 7. RENDER TEMPLATE ---
     processing_time = (datetime.now() - start_time).total_seconds()
     print(f"--- Request processing finished in {processing_time:.2f} seconds. Rendering template... ---")
     return render_template('dashboard.html',
                            symbol=symbol,
-                           current_price=current_price,
+                           current_price_display=current_price_display,
                            current_fng_value=current_fng_value,
-                           predicted_price_1h=predicted_price_1h,
-                           predicted_price_12h=predicted_price_12h,
-                           predicted_price_24h=predicted_price_24h,
-                           percentage_change_1h=percentage_change_1h,
-                           percentage_change_12h=percentage_change_12h,
-                           percentage_change_24h=percentage_change_24h,
-                           accuracy_metrics=accuracy_metrics,
-                           historical_accuracy_percent=historical_accuracy_percent,
+                           predicted_price_1h_display=predicted_price_1h_display,
+                           predicted_price_12h_display=predicted_price_12h_display,
+                           predicted_price_24h_display=predicted_price_24h_display,
+                           percentage_change_1h_display=percentage_change_1h_display,
+                           percentage_change_12h_display=percentage_change_12h_display,
+                           percentage_change_24h_display=percentage_change_24h_display,
                            data_confidence_score=data_confidence_score,
                            plot_data=plot_data,
+                           sources=sources,
                            error_message=error_message,
                            processing_time=f"{processing_time:.2f} sec",
-                           last_updated=datetime.now().strftime("%Y-%m-%d %H:%M:%S %Z")
-                           )
+                           last_updated=datetime.now().strftime("%Y-%m-%d %H:%M:%S %Z"))
 
-# --- Main execution block ---
 if __name__ == '__main__':
-    # Use host='0.0.0.0' to make accessible on local network
-    # Port 5000 is default, change if needed
-    # Debug=True provides more error detail and auto-reloads on code changes
     app.run(debug=True, host='0.0.0.0', port=5000)
\ No newline at end of file

static/styles.css

-/* static/styles.css */
-
-/* --- Global Styles & Fonts --- */
-@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700&display=swap');
-
-* {
-    margin: 0;
-    padding: 0;
-    box-sizing: border-box;
-}
-
 body {
-    font-family: 'Poppins', sans-serif;
-    background-color: #f4f7f6; /* Light grey background */
-    color: #333;
-    line-height: 1.6;
-}
-
-.container {
-    max-width: 1300px;
-    margin: 0 auto;
-    padding: 20px;
-}
-
-h1, h2, h3, h4, h5, h6 {
-    color: #2c3e50; /* Dark blue-grey */
-    margin-bottom: 0.75em;
-    margin-top: 1.25em;
-    font-weight: 600;
+    margin: 0;
+    font-family: 'Roboto', Arial, sans-serif;
+    background-color: #f5f7fa;
+    color: #0b527a;
 }
 
-h1 { font-size: 2.2rem; text-align: center; margin-top: 0.5em;}
-h2 { font-size: 1.6rem; border-bottom: 2px solid #e0e0e0; padding-bottom: 0.4em; margin-top: 2em;}
-h3 { font-size: 1.3rem; font-weight: 500; color: #34495e; }
-p { margin-bottom: 1em; color: #555; }
-a { color: #3498db; text-decoration: none; transition: color 0.3s ease; }
-a:hover { color: #2980b9; }
-hr { border: 0; border-top: 1px solid #eee; margin: 2em 0; }
-small { font-size: 0.85em; color: #777; }
-
-/* --- Splash Screen --- */
+/* Splash Screen */
 .splash-screen {
+    display: flex;
+    align-items: center;
+    justify-content: center;
     position: fixed;
     top: 0;
     left: 0;
     width: 100%;
     height: 100%;
-    /* Gradient background or solid color */
-    background: linear-gradient(135deg, #ffffff 0%, #e8f0f5 100%);
-    /* background-color: #fff; */
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    z-index: 10000; /* Ensure it's on top */
-    opacity: 1;
-    visibility: visible;
-    transition: opacity 0.3s ease-out, visibility 0s linear 0.3s; /* Match JS timing */
+    background-color: #0b527a;
+    z-index: 1000;
+    transition: opacity 1.5s ease, visibility 1.5s ease;
 }
 
 .splash-logo {
-    width: 250px; /* Adjust size as needed */
-    max-width: 70%;
-    height: auto;
-    opacity: 0;
-    transform: scale(0.8);
-    animation: splashFadeIn 1s ease-out 0.3s forwards, splashPulse 1.5s infinite alternate 1.3s;
-}
-
-@keyframes splashFadeIn {
-    to {
-        opacity: 1;
-        transform: scale(1);
-    }
+    width: 300px;
+    animation: fadeIn 1.5s ease;
 }
 
-@keyframes splashPulse {
-    from { transform: scale(1); }
-    to { transform: scale(1.03); }
+@keyframes fadeIn {
+    0% { opacity: 0; }
+    100% { opacity: 1; }
 }
 
-
-/* --- Header --- */
+/* Header */
 .main-header {
-    background: #ffffff; /* White header */
-    padding: 15px 30px;
     display: flex;
     justify-content: space-between;
     align-items: center;
-    box-shadow: 0 2px 10px rgba(0, 0, 0, 0.08);
-    position: sticky;
-    top: 0;
-    z-index: 1000; /* Below splash, above content */
-    border-bottom: 1px solid #eee;
+    background-color: #0b527a;
+    padding: 1rem 2rem;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
 }
 
-.logo-container {
-    display: flex;
-    align-items: center;
+.logo img {
+    height: 50px;
 }
 
-.header-logo {
-    height: 45px; /* Adjust as needed */
-    width: auto;
-    display: block; /* Prevents extra space below image */
+.dropdown {
+    position: relative;
 }
 
-.header-title {
-    font-size: 1.5rem;
-    font-weight: 600;
-    color: #34495e; /* Darker blue-grey */
-    margin-left: 15px;
+.dropdown-btn {
+    background: #fff;
+    border: none;
+    padding: 0.5rem 1rem;
+    font-size: 1rem;
+    cursor: pointer;
+    border-radius: 8px;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+    transition: background 0.3s ease;
 }
 
-.header-info span {
-    font-weight: 500;
-    color: #555;
+.dropdown-btn:hover {
+    background: #f5f7fa;
 }
 
-/* --- Main Content Sections --- */
-section {
-    background-color: #ffffff;
-    padding: 25px;
+.dropdown-content {
+    display: none;
+    position: absolute;
+    right: 0;
+    background-color: #fff;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
     border-radius: 8px;
-    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.06);
-    margin-bottom: 30px;
-}
-
-/* --- Status Section --- */
-.status-grid {
-    display: grid;
-    grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
-    gap: 20px;
-    margin-top: 15px;
+    overflow: hidden;
 }
 
-.status-grid > div {
-    background-color: #f8f9fa;
-    padding: 15px;
-    border-radius: 6px;
-    border: 1px solid #e9ecef;
+.dropdown:hover .dropdown-content {
+    display: block;
 }
 
-.status-grid .label {
+.dropdown-content a {
     display: block;
-    font-size: 0.9rem;
-    color: #6c757d;
-    margin-bottom: 5px;
-    font-weight: 500;
+    padding: 0.5rem 1rem;
+    color: #0b527a;
+    text-decoration: none;
+    transition: background 0.3s ease;
 }
 
-.status-grid .value {
-    font-size: 1.2rem;
-    font-weight: 600;
-    color: #343a40;
+.dropdown-content a:hover {
+    background-color: #6ba2c2;
+    color: #fff;
 }
-.status-grid .date-value {
-    font-size: 1rem;
-    font-weight: 500;
-}
-
 
-/* --- Chart Section --- */
-.chart-container {
-    position: relative;
-    height: 450px; /* Adjust height as needed */
-    width: 100%;
-    margin-top: 15px;
+/* Main Content */
+.current-status, .predictions, .data-sources {
+    padding: 1rem 2rem;
 }
-.chart-error {
-    text-align: center;
-    color: #dc3545;
-    font-weight: 500;
-    padding: 20px;
+
+.current-status h2, .predictions h3, .data-sources h3 {
+    color: #0b527a;
+    border-bottom: 2px solid #6ba2c2;
+    padding-bottom: 0.5rem;
 }
 
-/* --- Scores Section --- */
-.scores-grid {
-    display: grid;
-    grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
-    gap: 25px;
-    margin-top: 15px;
+.graphs-section {
+    display: flex;
+    justify-content: space-around;
+    padding: 2rem;
+    gap: 1rem;
 }
 
-.score-card {
-    background-color: #f8f9fa;
-    padding: 20px;
-    border-radius: 6px;
-    text-align: center;
-    border: 1px solid #e9ecef;
+.graph {
+    width: 45%;
+    padding: 1rem;
+    border: 1px solid #6ba2c2;
+    border-radius: 8px;
+    background-color: #fff;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+    transition: transform 0.3s ease;
 }
 
-.score-card h3 {
-    margin-top: 0;
-    margin-bottom: 10px;
-    font-size: 1.1rem;
-    color: #495057;
+.graph:hover {
+    transform: scale(1.02);
 }
 
-.score-value {
-    font-size: 2rem;
-    font-weight: 600;
-    color: #2c3e50;
-    margin-bottom: 5px;
+.graph h3 {
+    text-align: center;
+    margin-bottom: 1rem;
+    color: #0b527a;
 }
 
-.score-desc {
-    font-size: 0.85rem;
-    color: #6c757d;
-    margin-bottom: 0;
+.graph canvas {
+    height: 300px !important;
 }
 
-/* --- Predictions Section --- */
-.predictions-grid {
-    display: grid;
-    grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
-    gap: 20px;
-    margin-top: 15px;
+.predictions .prediction-item {
+    margin: 0.5rem 0;
 }
 
-.prediction-card {
-    background-color: #f8f9fa;
-    padding: 15px;
-    border-radius: 6px;
+.prediction-score {
     text-align: center;
-    border: 1px solid #e9ecef;
+    font-size: 1.2rem;
+    margin: 1rem 0;
+    color: #6ba2c2;
 }
 
-.prediction-card h3 {
-    margin-top: 0;
-    margin-bottom: 10px;
-    font-size: 1rem;
-    font-weight: 500;
+.prediction-score small {
+    color: #0b527a;
 }
 
-.prediction-price {
-    font-size: 1.4rem;
-    font-weight: 600;
-    margin-bottom: 5px;
-    color: #343a40;
+/* Data Sources */
+.data-sources ul {
+    list-style: none;
+    padding: 0;
+    max-height: 200px;
+    overflow-y: auto;
+    border: 1px solid #6ba2c2;
+    border-radius: 8px;
+    background-color: #fff;
 }
 
-.prediction-change {
-    font-size: 1.1rem;
-    font-weight: 500;
-    margin-bottom: 0;
+.data-sources li {
+    padding: 0.5rem 1rem;
+    border-bottom: 1px solid #f5f7fa;
+    transition: background 0.3s ease;
 }
 
-.prediction-change.positive { color: #28a745; }
-.prediction-change.negative { color: #dc3545; }
-
-/* --- Sources Section --- */
-.sources-section ul {
-    list-style-type: none;
-    padding-left: 0;
+.data-sources li:last-child {
+    border-bottom: none;
 }
 
-.sources-section li {
-    background-color: #f8f9fa;
-    margin-bottom: 8px;
-    padding: 10px 15px;
-    border-radius: 4px;
-    border: 1px solid #e9ecef;
-    font-weight: 500;
+.data-sources li:hover {
+    background-color: #6ba2c2;
+    color: #fff;
 }
 
-/* --- Banners & Disclaimers --- */
-.error-banner {
-    background-color: #f8d7da;
-    color: #721c24;
-    border: 1px solid #f5c6cb;
-    padding: 15px;
+/* Error Message */
+.error-message {
+    background-color: #ffe6e6;
+    color: #d8000c;
+    padding: 1rem;
+    margin: 1rem 2rem;
     border-radius: 8px;
-    margin-bottom: 25px;
+    text-align: center;
 }
-.error-banner p { margin-bottom: 0; }
 
-.disclaimer {
+/* Footer */
+.footer {
     text-align: center;
-    margin-top: 30px;
-    font-style: italic;
-    color: #6c757d;
+    padding: 1rem;
+    margin-top: 2rem;
+    border-top: 1px solid #6ba2c2;
+    color: #0b527a;
     font-size: 0.9rem;
 }
\ No newline at end of file
-
-/* --- Responsive Adjustments --- */
-@media (max-width: 768px) {
-    h1 { font-size: 1.8rem; }
-    h2 { font-size: 1.4rem; }
-    .main-header { padding: 10px 15px; flex-direction: column; gap: 10px;}
-    .header-title { font-size: 1.3rem; }
-    .header-logo { height: 40px; }
-    .container { padding: 15px; }
-    section { padding: 20px; }
-    .chart-container { height: 350px; }
-    .status-grid, .scores-grid, .predictions-grid { grid-template-columns: 1fr; } /* Stack on smaller screens */
-}
-