Base app.py interface v1.0

app.py

@@ -1,14 +1,15 @@
-import requests, pandas as pd
-from transformers import pipeline
-from datetime import datetime, timedelta
+# app.py — Financial RAG Dashboard
 import gradio as gr
+import asyncio
+import requests
+import pandas as pd
+from itertools import cycle
+from transformers import pipeline
+from datetime import datetime
 
-# ============================================================
-# 1️⃣ FRED API — экономические данные США1
-# ============================================================
-API_KEY = "YOUR_FRED_API_KEY"  # зарегистрируй на https://fred.stlouisfed.org/
+# 🔧 Настройки
+FRED_KEY = "YOUR_FRED_API_KEY"
 FRED_URL = "https://api.stlouisfed.org/fred/series/observations"
-
 INDICATORS = {
     "GDP": "GDP",
     "Inflation (CPI)": "CPIAUCSL",
@@ -16,63 +17,62 @@ INDICATORS = {
     "Interest Rate (Fed Funds)": "FEDFUNDS"
 }
 
-def fetch_fred_data(series_id, start="2024-01-01", end=None):
-    if end is None:
-        end = datetime.now().strftime("%Y-%m-%d")
+# 🧠 Модель генерации аналитики
+generator = pipeline("text2text-generation", model="google/flan-t5-base")
+
+# 🔁 Анимация “обработка...”
+async def async_loader(update_fn, delay=0.15):
+    frames = cycle(["⠋","⠙","⠹","⠸","⠼","⠴","⠦","⠧","⠇","⠏"])
+    for frame in frames:
+        update_fn(f"💭 Fetching FRED data {frame}")
+        await asyncio.sleep(delay)
+
+# 📈 Получение данных FRED
+def fetch_data(indicator):
+    series = INDICATORS[indicator]
     params = {
-        "series_id": series_id,
-        "api_key": API_KEY,
+        "series_id": series,
+        "api_key": FRED_KEY,
         "file_type": "json",
-        "observation_start": start,
-        "observation_end": end
+        "observation_start": "2024-01-01",
     }
-    r = requests.get(FRED_URL, params=params)
-    data = r.json().get("observations", [])
+    data = requests.get(FRED_URL, params=params).json().get("observations", [])
     df = pd.DataFrame(data)
-    if not df.empty:
-        df["value"] = pd.to_numeric(df["value"], errors="coerce")
-    return df
-
-# ============================================================
-# 2️⃣ Генерация сводки (LLM)
-# ============================================================
-generator = pipeline("text2text-generation", model="google/flan-t5-base")
+    df["value"] = pd.to_numeric(df["value"], errors="coerce")
+    return df.tail(8)
 
-def generate_summary(topic):
-    sid = INDICATORS.get(topic)
-    df = fetch_fred_data(sid)
+# 🧩 Аналитика и экспорт
+def generate_and_export(indicator):
+    df = fetch_data(indicator)
     if df.empty:
-        return "⚠️ No data available for this indicator."
-    last = df.tail(5)
-    trend = last["value"].pct_change().mean() * 100
-    context = f"Recent values of {topic}:\n{last[['date','value']].to_string(index=False)}\n\nAverage change: {trend:.2f}%"
-    prompt = f"Provide an analytical summary of the trend:\n{context}"
-    summary = generator(prompt, max_new_tokens=150)[0]["generated_text"]
-    return summary, df
+        return "⚠️ No data found.", None
 
-# ============================================================
-# 3️⃣ Экспорт CSV → Power BI
-# ============================================================
-def export_csv(df, topic):
-    filename = f"powerbi_{topic.replace(' ', '_').lower()}.csv"
-    df.to_csv(filename, index=False)
-    return filename
+    trend = df["value"].pct_change().mean() * 100
+    context = f"Recent {indicator} data:\n{df[['date','value']].to_string(index=False)}"
+    prompt = f"Analyze this economic indicator and summarize its recent trend:\n{context}\nAverage change: {trend:.2f}%"
+    summary = generator(prompt, max_new_tokens=120)[0]["generated_text"]
 
-# ============================================================
-# 4️⃣ Gradio UI
-# ============================================================
-with gr.Blocks(title="🏦 RAG Financial Analytics → Power BI") as app:
-    gr.Markdown("## 🏦 Financial RAG: FRED API → LLM → Power BI")
+    fname = f"powerbi_{indicator.lower().replace(' ','_')}.csv"
+    df.to_csv(fname, index=False)
+    return summary, fname
 
-    topic = gr.Dropdown(list(INDICATORS.keys()), label="Select Indicator", value="Inflation (CPI)")
-    out_summary = gr.Textbox(label="Generated Summary", lines=8)
-    out_file = gr.File(label="Power BI Export")
+# 🧱 Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft(), title="🏦 Financial RAG → Power BI") as demo:
+    gr.Markdown(
+        "## 🏦 Financial RAG Dashboard\n"
+        "Автоматическая аналитика по банковским данным (FRED API) с экспортом в Power BI.\n\n"
+        "_Интерфейс с потоковой анимацией и аккуратной вёрсткой._"
+    )
 
-    def run_pipeline(topic):
-        summary, df = generate_summary(topic)
-        file_path = export_csv(df, topic)
-        return summary, file_path
+    with gr.Row():
+        with gr.Column(scale=1):
+            indicator = gr.Dropdown(list(INDICATORS.keys()), label="Выберите показатель", value="Inflation (CPI)")
+            btn = gr.Button("📊 Получить аналитику", variant="primary")
+        with gr.Column(scale=2):
+            summary = gr.Textbox(label="📈 Сводка аналитики", lines=8)
+            export_file = gr.File(label="📂 Файл для Power BI")
 
-    gr.Button("Generate & Export").click(run_pipeline, inputs=topic, outputs=[out_summary, out_file])
+    # Обработчик
+    btn.click(generate_and_export, inputs=indicator, outputs=[summary, export_file])
 
-app.launch()
+demo.queue(max_size=32).launch(server_name="0.0.0.0", server_port=7860)