model change

app.py

@@ -4,6 +4,9 @@ from prompts import TRADELINK_CONTEXT
 from openai import OpenAI
 from analyzer import analyze_portfolio_streaming
 from comparer import compare_portfolio
+from table_view import get_metrics_dataframe
+import asyncio
+
 
 api_key = os.getenv("featherless")  #
 client = OpenAI(
@@ -11,6 +14,14 @@ client = OpenAI(
     api_key=api_key
 )
 
+def show_metrics_table(pid: str):
+    try:
+        df = asyncio.run(get_metrics_dataframe(pid))
+        return df
+    except Exception as e:
+        return f"❌ Ошибка: {e}"
+
+
 def handle_chat_streaming(user_input):
     response = client.chat.completions.create(
         model="meta-llama/Meta-Llama-3.1-8B-Instruct",
@@ -40,6 +51,7 @@ with gr.Blocks() as demo:
     gr.Markdown("## 🧠 Анализ и сравнение инвестиционных портфелей Tradelink")
 
     with gr.Tab("📊 Анализ"):
+
         portfolio_input = gr.Textbox(label="Введите ссылку или portfolioId", placeholder="ea856c1b-...")
         analyze_button = gr.Button("🔍 Проанализировать")
         output_box = gr.Textbox(label="📈 Результат анализа", lines=15)
@@ -63,5 +75,13 @@ with gr.Blocks() as demo:
 
         chat_button.click(fn=handle_chat_streaming, inputs=chat_input, outputs=chat_output)
 
+    with gr.Tab("📋 Метрики (таблица)"):
+        table_input = gr.Textbox(label="Введите portfolioId", placeholder="ea856c1b-...")
+        table_button = gr.Button("📥 Показать метрики")
+        table_output = gr.Dataframe(label="Таблица метрик", wrap=True)
+
+        table_button.click(fn=show_metrics_table, inputs=table_input, outputs=table_output)
+
+
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1,4 +1,5 @@
 gradio>=4.29.0
 openai>=1.30.1
 requests
-httpx
+httpx
+pandas

table_view.py

@@ -0,0 +1,16 @@
+import pandas as pd
+from fetch import extract_portfolio_id, fetch_metrics_async
+import asyncio
+
+async def get_metrics_dataframe(portfolio_id: str) -> pd.DataFrame:
+    pid = extract_portfolio_id(portfolio_id)
+    if not pid:
+        raise ValueError("Некорректный portfolioId")
+
+    metrics = await fetch_metrics_async(pid)
+    if not metrics:
+        raise ValueError("Метрики не найдены")
+
+    df = pd.DataFrame.from_dict(metrics, orient='index', columns=["Значение"])
+    df.index.name = "Метрика"
+    return df