Update app.py

app.py

@@ -1,64 +1,77 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+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
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# --- 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)}"]
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# --- 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."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+interface.launch()