Create Core Code

Core Code

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from scipy.fft import fft, fftfreq
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential, load_model
+import requests
+
+# --- Pre-trained Model (Simple LSTM) ---
+def build_model():
+    model = Sequential([
+        tf.keras.layers.LSTM(32, input_shape=(30, 1)),
+        tf.keras.layers.Dense(1)
+    ])
+    model.compile(loss='mse', optimizer='adam')
+    return model
+
+# --- Core Functions ---
+def analyze_data(data_url, prediction_days=30):
+    try:
+        # 1. Fetch data
+        df = pd.read_csv(data_url)
+        dates = df.columns[4:]  # COVID data format
+        values = df.drop(columns=['Province/State', 'Country/Region', 'Lat', 'Long']).sum(axis=0)[4:].values.astype(float)
+        
+        # 2. Detect cycles
+        N = len(values)
+        yf = fft(values)
+        xf = fftfreq(N, 1)[:N//2]
+        dominant_freq = xf[np.argmax(np.abs(yf[0:N//2]))]
+        cycle_days = int(1/dominant_freq)
+        
+        # 3. Make predictions (simplified)
+        scaler = MinMaxScaler()
+        scaled = scaler.fit_transform(values.reshape(-1, 1))
+        
+        model = build_model()
+        model.fit(scaled[:-10], scaled[10:], epochs=5, verbose=0)  # Quick training
+        
+        preds = model.predict(scaled[-30:].reshape(1, 30, 1))
+        preds = scaler.inverse_transform(preds).flatten().tolist()
+        
+        # 4. Generate insights
+        insights = [
+            f"Dominant cycle: {cycle_days} days",
+            f"Next {prediction_days}-day trend: {'↑ Upward' if preds[-1] > preds[0] else '↓ Downward'}",
+            "Action: Monitor closely around cycle peaks"
+        ]
+        
+        # Simple plot
+        plot = pd.DataFrame({
+            'Historical': values,
+            'Predicted': [None]*(len(values)) + preds
+        }).plot(title="Cases Analysis").figure
+        
+        return plot, insights
+    
+    except Exception as e:
+        return None, [f"Error: {str(e)}"]
+
+# --- Gradio Interface ---
+interface = gr.Interface(
+    fn=analyze_data,
+    inputs=[
+        gr.Textbox(label="Data URL", 
+                   value="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/data/time_series_covid19_confirmed_global.csv"),
+        gr.Number(label="Days to Predict", value=30)
+    ],
+    outputs=[
+        gr.Plot(label="Analysis"),
+        gr.JSON(label="Insights")
+    ],
+    title="DeepSeek Lite Analyzer",
+    description="Analyze time-series data from public URLs. Works best with COVID-19 format data."
+)
+
+interface.launch()