Test1

app.py

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+import requests, pandas as pd
+from transformers import pipeline
+from datetime import datetime, timedelta
+import gradio as gr
+
+# ============================================================
+# 1️⃣ FRED API — экономические данные США
+# ============================================================
+API_KEY = "YOUR_FRED_API_KEY"  # зарегистрируй на https://fred.stlouisfed.org/
+FRED_URL = "https://api.stlouisfed.org/fred/series/observations"
+
+INDICATORS = {
+    "GDP": "GDP",
+    "Inflation (CPI)": "CPIAUCSL",
+    "Unemployment Rate": "UNRATE",
+    "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")
+    params = {
+        "series_id": series_id,
+        "api_key": API_KEY,
+        "file_type": "json",
+        "observation_start": start,
+        "observation_end": end
+    }
+    r = requests.get(FRED_URL, params=params)
+    data = r.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")
+
+def generate_summary(topic):
+    sid = INDICATORS.get(topic)
+    df = fetch_fred_data(sid)
+    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
+
+# ============================================================
+# 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
+
+# ============================================================
+# 4️⃣ Gradio UI
+# ============================================================
+with gr.Blocks(title="🏦 RAG Financial Analytics → Power BI") as app:
+    gr.Markdown("## 🏦 Financial RAG: FRED API → LLM → Power BI")
+
+    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")
+
+    def run_pipeline(topic):
+        summary, df = generate_summary(topic)
+        file_path = export_csv(df, topic)
+        return summary, file_path
+
+    gr.Button("Generate & Export").click(run_pipeline, inputs=topic, outputs=[out_summary, out_file])
+
+app.launch()