Upload app.py

app.py

@@ -51,52 +51,41 @@ normalized_df = pd.DataFrame(normalized_data, columns=[col for col in data.colum
 normalized_df['year'] = data['year'].values
 
 # Функция предсказания
-def predict(territory, year, num_economactivepopulation_all, employed_num_all, unemployed_num_all,
-            eactivity_lvl, employment_lvl, dis_unagegroup_30_39, dis_emagegroup_30_39, 
-            num_unagegroup_30_39, num_emagegroup_30_39):
+
+def predict_unemployment(territory):
     if territory not in territory_mapping:
-        return "Invalid territory name. Please choose a valid one."
-
-    input_data = pd.DataFrame({
-        'territory': [territory_mapping[territory]],
-        'num_economactivepopulation_all': [num_economactivepopulation_all],
-        'employed_num_all': [employed_num_all],
-        'unemployed_num_all': [unemployed_num_all],
-        'eactivity_lvl': [eactivity_lvl],
-        'employment_lvl': [employment_lvl],
-        'dis_unagegroup_30-39': [dis_unagegroup_30_39],
-        'dis_emagegroup_30-39': [dis_emagegroup_30_39],
-        'num_unagegroup_30-39': [num_unagegroup_30_39],
-        'num_emagegroup_30-39': [num_emagegroup_30_39]
-    })
-
-    # Нормализация входных данных
-    input_normalized = scaler.transform(input_data)
-    input_sequence = np.expand_dims(input_normalized, axis=0)
-
-    # Прогноз
-    prediction = model.predict(input_sequence)
-    return round(prediction[0][0], 2)
+        return "Неверное название территории. Пожалуйста, выберите из списка."
+
+    predictions = []
+    current_year = data['year'].max() + 1
+
+    for i in range(5):
+        sample_row = data[data['territory'] == territory_mapping[territory]].iloc[-1].copy()
+        sample_row['year'] = current_year
+        input_data = sample_row.drop('year').values.reshape(1, -1)
+
+        # Нормализация входных данных
+        input_normalized = scaler.transform(input_data)
+        input_sequence = np.expand_dims(input_normalized, axis=0)
+
+        # Прогноз
+        prediction = model.predict(input_sequence)
+        predictions.append((current_year, round(prediction[0][0] * 100, 2)))
+
+        # Обновление года
+        current_year += 1
+
+    return "\n".join([f"{year}: {value}%" for year, value in predictions])
 
 # Интерфейс Gradio
 interface = gr.Interface(
-    fn=predict,
+    fn=predict_unemployment,
     inputs=[
-        gr.Dropdown(label="Territory", choices=list(territory_mapping.keys())),
-        gr.Number(label="Year"),
-        gr.Number(label="Economic Active Population"),
-        gr.Number(label="Employed Population"),
-        gr.Number(label="Unemployed Population"),
-        gr.Number(label="Economic Activity Level"),
-        gr.Number(label="Employment Level"),
-        gr.Number(label="Disunemployed Age Group 30-39"),
-        gr.Number(label="Disemployed Age Group 30-39"),
-        gr.Number(label="Unemployed Age Group 30-39"),
-        gr.Number(label="Employed Age Group 30-39")
+        gr.Dropdown(label="Территория", choices=list(territory_mapping.keys()))
     ],
-    outputs=gr.Textbox(label="Predicted Value"),
-    title="Unemployment Prediction Model",
-    description="Введите значения для прогноза уровня безработицы."
+    outputs=gr.Textbox(label="Прогноз на ближайшие 5 лет"),
+    title="Модель прогнозирования уровня безработицы",
+    description="Выберите территорию для прогноза уровня безработицы на ближайшие 5 лет."
 )
 
 interface.launch()