Merge pull request #1 from QAway-to/mainv1

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/TradeLinkAI.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="py.test" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="MarkdownIncorrectTableFormatting" enabled="false" level="WEAK WARNING" enabled_by_default="false" />
+    <inspection_tool class="MarkdownIncorrectlyNumberedListItem" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="MarkdownLinkDestinationWithSpaces" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="MarkdownNoTableBorders" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="MarkdownOutdatedTableOfContents" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="MarkdownUnresolvedFileReference" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="MarkdownUnresolvedHeaderReference" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="MarkdownUnresolvedLinkLabel" enabled="false" level="WARNING" enabled_by_default="false" />
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12 (sdfs11)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 (TradeLinkAI)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/TradeLinkAI.iml" filepath="$PROJECT_DIR$/.idea/TradeLinkAI.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

README.md

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+---
+title: Finacial Assistance
+emoji: 📊
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 5.36.2
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from services.llm_client import llm_service
+from core.analyzer import PortfolioAnalyzer
+from core.comparer import PortfolioComparer
+from core.chat import ChatAssistant
+from core.metrics import show_metrics_table
+from core.crypto_dashboard import build_crypto_dashboard  # Plotly dashboard + KPI-line
+from core.visual_comparison import build_price_chart, build_volatility_chart  # Interactive pair comparison
+
+# === CSS loader ===
+def load_css(path: str) -> str:
+    with open(path, "r", encoding="utf-8") as f:
+        return f.read()
+
+# === Styles ===
+base_css   = load_css("core/styles/base.css")
+crypto_css = load_css("core/styles/crypto_dashboard.css")
+
+# === Model setup ===
+MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+analyzer = PortfolioAnalyzer(llm_service, MODEL_NAME)
+comparer = PortfolioComparer(llm_service, MODEL_NAME)
+chatbot = ChatAssistant(llm_service, MODEL_NAME)
+
+# === Main Interface ===
+with gr.Blocks(css=base_css) as demo:
+    gr.Markdown("## Investment Portfolio Analyzer")
+    gr.Markdown(
+        "Professional AI-driven analytics for investment and crypto markets.",
+        elem_classes="subtitle",
+    )
+
+    with gr.Tabs():
+        # --- Analysis ---
+        with gr.TabItem("Analysis"):
+            portfolio_input = gr.Textbox(
+                label="Portfolio ID or Link",
+                placeholder="Enter portfolio ID (uuid)",
+                value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb",
+            )
+            analyze_btn = gr.Button("Run Analysis", variant="primary")
+            analyze_out = gr.Textbox(label="Analysis Result", lines=15, elem_id="analysis_output")
+            analyze_btn.click(fn=analyzer.run, inputs=portfolio_input, outputs=analyze_out)
+
+        # --- Comparison Table ---
+        with gr.TabItem("Comparison Table"):
+            from core.comparison_table import show_comparison_table
+            pid_a = gr.Textbox(label="Portfolio A", value="3852a354-e66e-4bc5-97e9-55124e31e687")
+            pid_b = gr.Textbox(label="Portfolio B", value="b1ef37aa-5b9a-41b4-8823f2de36bb")
+            compare_btn = gr.Button("Load Comparison", variant="primary")
+            comp_table = gr.Dataframe(label="Comparative Metrics", wrap=True)
+            comp_comment = gr.Textbox(label="AI Commentary", lines=14, elem_id="llm_comment_box")
+            compare_btn.click(fn=show_comparison_table, inputs=[pid_a, pid_b], outputs=[comp_table, comp_comment])
+
+        # --- Assistant ---
+        with gr.TabItem("Assistant"):
+            chat_in = gr.Textbox(label="Ask about investments or analysis")
+            chat_btn = gr.Button("Send Question", variant="primary")
+            chat_out = gr.Textbox(label="AI Response", lines=8)
+            chat_btn.click(fn=chatbot.run, inputs=chat_in, outputs=chat_out)
+
+        # --- Metrics Table ---
+        with gr.TabItem("Metrics Table"):
+            metrics_in = gr.Textbox(label="Portfolio ID", value="b1ef37aa-5b9a-41b4-9394-8823f2de36bb")
+            metrics_btn = gr.Button("Load Metrics", variant="primary")
+            metrics_out = gr.Dataframe(label="Portfolio Metrics", wrap=True)
+            metrics_btn.click(fn=show_metrics_table, inputs=metrics_in, outputs=metrics_out)
+
+        # --- Visual Comparison (Interactive Plotly Edition) ---
+        with gr.TabItem("Visual Comparison"):
+            gr.Markdown("### 📊 Market Pair Comparison — Interactive Plotly Edition")
+
+            available_pairs = [
+                ("bitcoin", "ethereum"),
+                ("bitcoin", "solana"),
+                ("ethereum", "bnb"),
+                ("solana", "dogecoin"),
+                ("bitcoin", "toncoin"),
+            ]
+
+            pair_selector = gr.Dropdown(
+                label="Select Pair for Comparison",
+                choices=[f"{a.capitalize()} vs {b.capitalize()}" for a, b in available_pairs],
+                value="Bitcoin vs Ethereum",
+                interactive=True,
+            )
+
+            price_plot = gr.Plot(label="Price Comparison")
+            vol_plot = gr.Plot(label="Volatility Comparison")
+
+            def update_visuals(selected_pair: str):
+                for a, b in available_pairs:
+                    if selected_pair.lower() == f"{a} vs {b}":
+                        return build_price_chart((a, b)), build_volatility_chart((a, b))
+                return build_price_chart(("bitcoin", "ethereum")), build_volatility_chart(("bitcoin", "ethereum"))
+
+            pair_selector.change(fn=update_visuals, inputs=pair_selector, outputs=[price_plot, vol_plot])
+
+            def init_visuals():
+                return update_visuals("Bitcoin vs Ethereum")
+
+            demo.load(fn=init_visuals, inputs=None, outputs=[price_plot, vol_plot])
+
+        # --- Crypto Intelligence Dashboard (Plotly Edition + KPI line, auto-load) ---
+        with gr.TabItem("Crypto Intelligence Dashboard"):
+            gr.HTML(f"<style>{crypto_css}</style>")
+
+            with gr.Row(elem_id="kpi_row"):
+                kpi_line = gr.HTML(elem_id="kpi_line")
+
+            with gr.Row(elem_id="controls_row"):
+                top_slider = gr.Slider(
+                    label="Top N coins",
+                    minimum=20, maximum=100, step=10, value=50, scale=100,
+                )
+
+            with gr.Row(equal_height=True, elem_id="main_charts_row"):
+                with gr.Column(scale=70):
+                    treemap_plot = gr.Plot(label="Market Composition")
+                with gr.Column(scale=30):
+                    ai_box = gr.Textbox(label="AI Market Summary", lines=18, elem_id="ai_summary_sidebar")
+
+            with gr.Row(equal_height=True, elem_id="bottom_charts_row"):
+                movers_plot = gr.Plot(label="Top Movers", scale=50)
+                scatter_plot = gr.Plot(label="Market Cap vs Volume", scale=50)
+
+            def run_dash(n):
+                fig_treemap, fig_bar, fig_bubble, ai_comment, kpi_text = build_crypto_dashboard(n)
+                return fig_treemap, fig_bar, fig_bubble, ai_comment, f"<div class='kpi-line'>{kpi_text}</div>"
+
+            top_slider.change(
+                fn=run_dash,
+                inputs=top_slider,
+                outputs=[treemap_plot, movers_plot, scatter_plot, ai_box, kpi_line],
+                show_progress="minimal",
+            )
+
+            def init_crypto():
+                return run_dash(50)
+
+            demo.load(
+                fn=init_crypto,
+                inputs=None,
+                outputs=[treemap_plot, movers_plot, scatter_plot, ai_box, kpi_line],
+            )
+
+    gr.Markdown("---")
+    gr.Markdown(
+        "<center><small style='color:#6e7681;'>Developed with Featherless.ai • Powered by OpenAI-compatible API</small></center>",
+        elem_classes="footer",
+    )
+
+if __name__ == "__main__":
+    demo.launch()

config.py

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+"""
+🇬🇧 Module: config.py
+Purpose: Central configuration for environment variables and constants.
+
+🇷🇺 Модуль: config.py
+Назначение: централизованная конфигурация переменных окружения и констант проекта.
+"""
+
+import os
+
+# === Featherless.ai Configuration ===
+FEATHERLESS_API_KEY = os.getenv("featherless")
+FEATHERLESS_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+
+# === External API Configuration ===
+EXTERNAL_API_URL = os.getenv("EXTERNAL_API_URL")
+
+# === Request / Connection Settings ===
+REQUEST_TIMEOUT = 15
+DEBUG = os.getenv("DEBUG", "false").lower() == "true"

core/__init__.py

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+# __init__.py
+# Marks this directory as a Python package.

core/analyzer.py

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+"""
+🇬🇧 Module: analyzer.py()
+Purpose: Handles single-portfolio analysis using LLM. Fetches metrics, builds prompt, streams reasoning.
+
+🇷🇺 Модуль: analyzer.py
+Назначение: анализ одного инвестиционного портфеля с использованием LLM. Получает метрики, формирует промпт, возвращает потоковый ответ.
+"""
+
+import asyncio
+from typing import Generator
+from services.output_api import extract_portfolio_id, fetch_metrics_async
+from services.llm_client import llm_service
+from prompts.system_prompts import ANALYSIS_SYSTEM_PROMPT
+from prompts.reference_templates import REFERENCE_PROMPT
+
+
+class PortfolioAnalyzer:
+    """Main use-case class for analyzing a single portfolio."""
+
+    def __init__(self, llm=llm_service, model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"):
+        self.llm = llm
+        self.model_name = model_name
+
+    def run(self, text: str) -> Generator[str, None, None]:
+        """Stream analysis result step by step."""
+        portfolio_id = extract_portfolio_id(text)
+        if not portfolio_id:
+            yield "❗ Please enter a portfolio ID."
+            return
+
+        yield "⏳ Working..."
+        try:
+            metrics = asyncio.run(fetch_metrics_async(portfolio_id))
+        except Exception as e:
+            yield f"❌ Fail to collect metrics: {e}"
+            return
+
+        if not metrics:
+            yield "❗ Metrics can't be collected."
+            return
+
+        metrics_text = ", ".join(f"{k}: {v}" for k, v in metrics.items())
+        prompt = f"{REFERENCE_PROMPT}\n\nИспользуй эти данные для анализа:\n{metrics_text}"
+
+        try:
+            messages = [
+                {"role": "system", "content": ANALYSIS_SYSTEM_PROMPT},
+                {"role": "user", "content": prompt},
+            ]
+
+            partial = ""
+            for delta in self.llm.stream_chat(messages=messages, model=self.model_name):
+                partial += delta
+                yield partial
+
+        except Exception as e:
+            yield f"❌ LLM's error: {e}"

core/chat.py

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+"""
+🇬🇧 Module: chat.py
+Purpose: General chat interface for user questions about investments or portfolios.
+
+🇷🇺 Модуль: chat.py
+Назначение: общий чат-помощник для ответов на вопросы об инвестициях и портфелях.
+"""
+
+from typing import Generator
+from services.llm_client import llm_service
+from prompts.system_prompts import GENERAL_CONTEXT
+
+
+class ChatAssistant:
+    """Handles general user dialogue via LLM."""
+
+    def __init__(self, llm=llm_service, model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"):
+        self.llm = llm
+        self.model_name = model_name
+
+    def run(self, user_input: str) -> Generator[str, None, None]:
+        """Stream chat responses."""
+        messages = [
+            {"role": "system", "content": GENERAL_CONTEXT},
+            {"role": "user", "content": user_input},
+        ]
+
+        try:
+            partial = ""
+            for delta in self.llm.stream_chat(messages=messages, model=self.model_name):
+                partial += delta
+                yield partial
+
+        except Exception as e:
+            yield f"❌ Ошибка при генерации ответа: {e}"

core/comparer.py

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+"""
+🇬🇧 Module: comparer.py()
+Purpose: Compares two portfolios using LLM. Fetches metrics for both and builds a unified comparison prompt.
+
+🇷🇺 Модуль: comparer.py
+Назначение: сравнение двух инвестиционных портфелей с помощью LLM. Получает метрики обоих портфелей, формирует промпт и возвращает потоковый результат.
+"""
+
+import asyncio
+from typing import Generator
+from services.output_api import extract_portfolio_id, fetch_metrics_async
+from services.llm_client import llm_service
+from prompts.system_prompts import COMPARISON_SYSTEM_PROMPT
+from prompts.reference_templates import REFERENCE_COMPARISON_PROMPT
+
+
+class PortfolioComparer:
+    """Main use-case class for comparing two portfolios."""
+
+    def __init__(self, llm=llm_service, model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"):
+        self.llm = llm
+        self.model_name = model_name
+
+    def run(self, text1: str, text2: str) -> Generator[str, None, None]:
+        """Stream comparison results between two portfolios."""
+        id1 = extract_portfolio_id(text1)
+        id2 = extract_portfolio_id(text2)
+
+        if text1 == text2:
+            yield "❗ Please, give me a two difference portfolio ID."
+            return
+        if not id1 or not id2:
+            yield "❗ One of two portfolios is empty or incorrect."
+            return
+
+        yield "⏳ Working..."
+        try:
+            m1 = asyncio.run(fetch_metrics_async(id1))
+            m2 = asyncio.run(fetch_metrics_async(id2))
+        except Exception as e:
+            yield f"❌ There are issue via collecting data: {e}"
+            return
+
+        if not m1 or not m2:
+            yield "❗ One of two portfolios is empty or has incorrect data."
+            return
+
+        m1_text = ", ".join(f"{k}: {v}" for k, v in m1.items())
+        m2_text = ", ".join(f"{k}: {v}" for k, v in m2.items())
+
+        prompt = (
+            f"{REFERENCE_COMPARISON_PROMPT}\n"
+            f"Используй эти данные для сравнения:\n"
+            f"Портфель A: {m1_text}\n"
+            f"Портфель B: {m2_text}"
+        )
+
+        try:
+            messages = [
+                {"role": "system", "content": COMPARISON_SYSTEM_PROMPT},
+                {"role": "user", "content": prompt},
+            ]
+
+            partial = ""
+            for delta in self.llm.stream_chat(messages=messages, model=self.model_name):
+                partial += delta
+                yield partial
+
+        except Exception as e:
+            yield f"❌ Ошибка при сравнении портфелей через LLM: {e}"

core/comparison_table.py

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+"""
+🇬🇧 Module: comparison_table.py
+Purpose: Generates comparative DataFrame for two portfolios and an LLM commentary.
+
+🇷🇺 Модуль: comparison_table.py
+Назначение: создаёт сравнительную таблицу метрик двух портфелей и комментарий LLM.
+"""
+
+import pandas as pd
+import asyncio
+from services.output_api import fetch_metrics_async, extract_portfolio_id
+from services.llm_client import llm_service
+from prompts.system_prompts import COMPARISON_SYSTEM_PROMPT
+
+
+def show_comparison_table(portfolio_a: str, portfolio_b: str):
+    """Public Gradio entry: returns both a DataFrame and LLM commentary."""
+    pid_a = extract_portfolio_id(portfolio_a)
+    pid_b = extract_portfolio_id(portfolio_b)
+    if not pid_a or not pid_b:
+        return "❌ Invalid portfolio IDs.", "No commentary available."
+
+    try:
+        df, commentary = asyncio.run(_build_comparison_with_comment(pid_a, pid_b))
+        return df, commentary
+    except Exception as e:
+        return f"❌ Error building comparison table: {e}", "❌ LLM analysis failed."
+
+
+async def _build_comparison_with_comment(p1: str, p2: str):
+    """Async helper: builds table and gets commentary."""
+    m1 = await fetch_metrics_async(p1)
+    m2 = await fetch_metrics_async(p2)
+    if not m1 or not m2:
+        raise ValueError("Metrics unavailable for one or both portfolios.")
+
+    all_keys = sorted(set(m1.keys()) | set(m2.keys()))
+    rows = []
+    for k in all_keys:
+        v1 = m1.get(k, 0)
+        v2 = m2.get(k, 0)
+        diff = v1 - v2
+        symbol = "▲" if diff > 0 else "▼" if diff < 0 else "—"
+        rows.append({
+            "Metric": k,
+            "Portfolio A": round(v1, 3),
+            "Portfolio B": round(v2, 3),
+            "Δ Difference": f"{symbol} {diff:+.3f}"
+        })
+    df = pd.DataFrame(rows, columns=["Metric", "Portfolio A", "Portfolio B", "Δ Difference"])
+
+    # Generate LLM commentary
+    summary = "\n".join(f"{r['Metric']}: {r['Δ Difference']}" for r in rows)
+    prompt = (
+        f"{COMPARISON_SYSTEM_PROMPT}\n"
+        f"Compare and explain the differences between Portfolio A and B:\n{summary}\n"
+        f"Write your insights as a concise professional commentary."
+    )
+
+    commentary = ""
+    for delta in llm_service.stream_chat(
+        messages=[
+            {"role": "system", "content": "You are an investment portfolio analyst."},
+            {"role": "user", "content": prompt},
+        ],
+        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
+    ):
+        commentary += delta
+
+    return df, commentary

core/crypto_dashboard.py

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+"""
+Crypto Dashboard — Plotly Edition (clean layout)
+• убраны colorbar заголовки (percent_change_*)
+• уменьшены отступы KPI
+• без глобального Markdown-заголовка
+"""
+import requests
+import pandas as pd
+import plotly.express as px
+from services.llm_client import llm_service
+
+
+def fetch_coinlore_data(limit=100):
+    url = "https://api.coinlore.net/api/tickers/"
+    data = requests.get(url).json()["data"]
+    df = pd.DataFrame(data)
+    for col in ["price_usd", "market_cap_usd", "volume24",
+                "percent_change_1h", "percent_change_24h", "percent_change_7d"]:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+    return df.head(limit)
+
+
+def _kpi_line(df) -> str:
+    """Формирует компактную KPI-строку без лишних пробелов"""
+    tracked = ["BTC", "ETH", "SOL", "DOGE"]
+    parts = []
+    for sym in tracked:
+        row = df[df["symbol"] == sym]
+        if row.empty:
+            continue
+        price = float(row["price_usd"])
+        ch = float(row["percent_change_24h"])
+        arrow = "↑" if ch > 0 else "↓"
+        color = "#4ade80" if ch > 0 else "#f87171"
+        parts.append(
+            f"<b>{sym}</b> ${price:,.0f} "
+            f"<span style='color:{color}'>{arrow} {abs(ch):.2f}%</span>"
+        )
+    return " , ".join(parts)
+
+
+def build_crypto_dashboard(top_n=50):
+    df = fetch_coinlore_data(top_n)
+
+    # === Treemap ===
+    fig_treemap = px.treemap(
+        df,
+        path=["symbol"],
+        values="market_cap_usd",
+        color="percent_change_24h",
+        color_continuous_scale="RdYlGn",
+        height=420,
+    )
+    fig_treemap.update_layout(
+        title=None,
+        template="plotly_dark",
+        coloraxis_colorbar=dict(title=None),  # 🔹 убираем надпись percent_change_24h
+        margin=dict(l=5, r=5, t=5, b=5),
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+    )
+
+    # === Bar chart (Top gainers) ===
+    top = df.sort_values("percent_change_24h", ascending=False).head(12)
+    fig_bar = px.bar(
+        top,
+        x="percent_change_24h",
+        y="symbol",
+        orientation="h",
+        color="percent_change_24h",
+        color_continuous_scale="Blues",
+        height=320,
+    )
+    fig_bar.update_layout(
+        title=None,
+        template="plotly_dark",
+        coloraxis_colorbar=dict(title=None),  # 🔹 убираем надпись percent_change_24h
+        margin=dict(l=40, r=10, t=5, b=18),
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+    )
+
+    # === Scatter (Market Cap vs Volume) ===
+    fig_bubble = px.scatter(
+        df.head(60),
+        x="market_cap_usd",
+        y="volume24",
+        size="price_usd",
+        color="percent_change_7d",
+        hover_name="symbol",
+        log_x=True,
+        log_y=True,
+        color_continuous_scale="RdYlGn",
+        height=320,
+    )
+    fig_bubble.update_layout(
+        title=None,
+        template="plotly_dark",
+        coloraxis_colorbar=dict(title=None),  # 🔹 убираем надпись percent_change_7d
+        margin=dict(l=36, r=10, t=5, b=18),
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+    )
+
+    # === LLM summary ===
+    summary = _ai_summary(df)
+    kpi_text = _kpi_line(df)
+    return fig_treemap, fig_bar, fig_bubble, summary, kpi_text
+
+
+def _ai_summary(df):
+    leaders = df.sort_values("percent_change_24h", ascending=False).head(3)["symbol"].tolist()
+    laggards = df.sort_values("percent_change_24h").head(3)["symbol"].tolist()
+    prompt = f"""
+Summarize today's crypto market based on Coinlore data.
+Top gainers: {', '.join(leaders)}.
+Top losers: {', '.join(laggards)}.
+Include: overall sentiment, volatility/liquidity, short-term outlook.
+"""
+    text = ""
+    for delta in llm_service.stream_chat(
+        messages=[{"role": "user", "content": prompt}],
+        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
+    ):
+        text += delta
+    return text

core/data_binance.py

@@ -0,0 +1,22 @@
+"""
+Binance API Handler – public OHLC data
+"""
+import requests, pandas as pd
+
+BINANCE = "https://api.binance.com/api/v3"
+
+def get_binance_data(symbols=None, limit=500):
+    if symbols is None:
+        symbols = ["BTCUSDT", "ETHUSDT", "BNBUSDT"]
+    frames = []
+    for s in symbols:
+        url = f"{BINANCE}/klines?symbol={s}&interval=1d&limit={limit}"
+        r = requests.get(url).json()
+        df = pd.DataFrame(r, columns=[
+            "t","o","h","l","c","v","ct","qv","tr","tb","tbv","ig"])
+        df = df[["t","c"]].rename(columns={"t":"timestamp","c":"close"})
+        df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
+        df["close"] = df["close"].astype(float)
+        df["symbol"] = s
+        frames.append(df)
+    return pd.concat(frames)

core/data_coinlore.py

@@ -0,0 +1,13 @@
+"""
+Coinlore API Handler – market overview
+"""
+import requests, pandas as pd
+
+def get_coinlore_data():
+    url = "https://api.coinlore.net/api/tickers/"
+    data = requests.get(url).json()["data"]
+    df = pd.DataFrame(data)
+    df["price_usd"] = df["price_usd"].astype(float)
+    df["market_cap_usd"] = df["market_cap_usd"].astype(float)
+    df["percent_change_24h"] = df["percent_change_24h"].astype(float)
+    return df

core/data_yfinance.py

@@ -0,0 +1,16 @@
+"""
+Yahoo Finance API Handler – historical 5-10 years
+"""
+import yfinance as yf, pandas as pd
+
+def get_yf_history(symbols=None, period="2y"):
+    if symbols is None:
+        symbols = ["BTC-USD","ETH-USD","BNB-USD"]
+    frames = []
+    for s in symbols:
+        df = yf.download(s, period=period, interval="1d", progress=False)
+        df = df.reset_index()[["Date","Close"]]
+        df.columns = ["timestamp","close"]
+        df["symbol"] = s.replace("-USD","USDT")
+        frames.append(df)
+    return pd.concat(frames)

core/metrics.py

@@ -0,0 +1,34 @@
+"""
+🇬🇧 Module: metrics.py
+Purpose: Provides async utilities to fetch and display portfolio metrics as a DataFrame.
+
+🇷🇺 Модуль: metrics.py
+Назначение: предоставляет асинхронные функции для получения и отображения метрик портфеля в виде DataFrame.
+"""
+
+import pandas as pd
+import asyncio
+from services.output_api import extract_portfolio_id, fetch_metrics_async
+
+
+def show_metrics_table(portfolio_input: str):
+    """Fetch portfolio metrics and return them as a DataFrame for Gradio."""
+    pid = extract_portfolio_id(portfolio_input)
+    if not pid:
+        return "❌ Invalid portfolioId format."
+
+    try:
+        df = asyncio.run(_get_metrics_df(pid))
+        return df
+    except Exception as e:
+        return f"❌ Error fetching metrics: {e}"
+
+
+async def _get_metrics_df(portfolio_id: str) -> pd.DataFrame:
+    """Internal helper to asynchronously get metrics."""
+    metrics = await fetch_metrics_async(portfolio_id)
+    if not metrics:
+        raise ValueError("No metrics found for given portfolio.")
+
+    df = pd.DataFrame(list(metrics.items()), columns=["Metric", "Value"])
+    return df

core/styles/base.css

@@ -0,0 +1,18 @@
+/* === Global Layout === */
+.gradio-container { font-family: 'Inter', sans-serif; background:#0d1117 !important; }
+[data-testid="block-container"] { max-width:1180px !important; margin:auto !important; }
+h2, h3, .gr-markdown { color:#f0f6fc !important; font-weight:600 !important; }
+
+/* buttons / slider */
+.gr-button {
+  border-radius:6px !important; font-weight:600 !important; height:52px !important;
+  background:linear-gradient(90deg,#4f46e5,#6366f1) !important; border:none !important;
+  box-shadow:0 2px 4px rgba(0,0,0,.25);
+}
+.gr-slider { height:52px !important; }
+.gr-slider input[type=range]::-webkit-slider-thumb { background:#6366f1 !important; }
+
+/* tables */
+.gr-dataframe table { width:100% !important; color:#c9d1d9 !important; background:#161b22 !important; }
+.gr-dataframe th { background:#21262d !important; color:#f0f6fc !important; border-bottom:1px solid #30363d !important; }
+.gr-dataframe td { border-top:1px solid #30363d !important; padding:8px !important; }

core/styles/crypto_dashboard.css

@@ -0,0 +1,132 @@
+/* === Убираем отступ между вкладками и контентом === */
+[data-testid="tabitem"] {
+  margin-top: 0 !important;
+  padding-top: 0 !important;
+}
+
+/* === KPI fixed line (строка с метриками) === */
+#kpi_row {
+  margin-top: 0 !important;
+  margin-bottom: 0 !important;
+  padding: 0 !important;
+}
+#kpi_line {
+  width: 100%;
+  margin: 0 !important;
+  padding: 2px 6px !important;
+  background: transparent;
+  color: #f0f6fc;
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 14px;
+  line-height: 1.2;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  display: flex;
+  align-items: center;
+  justify-content: flex-start;
+  min-height: 24px;
+  border-bottom: 1px solid #30363d;
+  transition: all 0.2s ease-in-out;
+}
+
+/* KPI items */
+.kpi-item {
+  margin-right: 14px;
+  letter-spacing: 0.2px;
+  transition: opacity 0.2s ease;
+}
+.kpi-item:hover {
+  opacity: 0.85;
+}
+
+/* === Controls (slider + button) — минимальные отступы === */
+#controls_row {
+  margin-top: 2px !important;
+  margin-bottom: 2px !important;
+  padding: 0 !important;
+}
+#controls_row .gr-button,
+#controls_row .gr-slider {
+  margin-top: 0 !important;
+  margin-bottom: 0 !important;
+}
+
+/* === Plot containers — плотное выравнивание === */
+[data-testid="plot-container"] {
+  width: 100% !important;
+  margin: 0 !important;
+  padding: 0 !important;
+  background: transparent !important;
+  border: none !important;
+}
+[data-testid="plot-container"] .wrap {
+  margin: 0 !important;
+  padding: 0 !important;
+  background: transparent !important;
+  box-shadow: none !important;
+}
+[data-testid="plot-container"] svg {
+  display: none !important;
+}
+
+/* === Скрываем только встроенные заголовки Plotly и подписи colorbar === */
+.js-plotly-plot .plotly .gtitle,
+.js-plotly-plot .plotly .g-xtitle,
+.js-plotly-plot .plotly .g-ytitle,
+.js-plotly-plot .colorbar-title,
+.js-plotly-plot .colorbar .cbtitle,
+.js-plotly-plot .colorbar .cbTitle {
+  display: none !important;
+  visibility: hidden !important;
+}
+
+/* === Явно оставляем видимыми подписи осей, тики, легенду и аннотации === */
+.js-plotly-plot .legend text,
+.js-plotly-plot .xtick text,
+.js-plotly-plot .ytick text,
+.js-plotly-plot .annotation text,
+.js-plotly-plot .colorbar .tick text {
+  display: block !important;
+  visibility: visible !important;
+}
+
+/* === Plot canvas плотные высоты === */
+#root [label="Market Composition"] canvas {
+  height: 440px !important;
+  margin: 0 !important;
+}
+#root [label="Top Movers"] canvas,
+#root [label="Market Cap vs Volume"] canvas {
+  height: 340px !important;
+  margin: 0 !important;
+}
+
+/* === Убираем возможные системные бордеры блоков Gradio === */
+[data-testid="block"] {
+  border: none !important;
+  box-shadow: none !important;
+  background: transparent !important;
+}
+
+/* === AI sidebar === */
+#ai_summary_sidebar textarea {
+  height: 440px !important;
+  background: #161b22 !important;
+  color: #f0f6fc !important;
+  border: 1px solid #30363d !important;
+  border-radius: 6px !important;
+  font-family: 'JetBrains Mono', monospace !important;
+  font-size: 13.5px !important;
+  line-height: 1.55 !important;
+  padding: 12px !important;
+  resize: none !important;
+  box-shadow: inset 0 0 3px rgba(0,0,0,0.25);
+}
+
+/* === Ряды между графиками === */
+.gr-row {
+  gap: 10px !important;
+  margin-top: 2px !important;
+  margin-bottom: 2px !important;
+}

core/styles/multi_charts.css

@@ -0,0 +1,4 @@
+[data-testid="html"]{
+  width:100% !important; background:#0d1117 !important; border:1px solid #30363d !important;
+  border-radius:6px !important; overflow:hidden !important; min-height: 360px;
+}

core/ui_style.css

@@ -0,0 +1,135 @@
+/* === Global Layout === */
+.gradio-container {
+    font-family: 'Inter', sans-serif;
+    background-color: #0d1117 !important;
+}
+[data-testid="block-container"] {
+    max-width: 1180px !important;
+    margin: auto !important;
+}
+
+/* === Typography === */
+h2, h3, .gr-markdown {
+    color: #f0f6fc !important;
+    font-weight: 600 !important;
+}
+
+/* === KPI Panel === */
+#kpi_panel {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin: 12px 0 4px 0;
+}
+.kpi-card {
+    background-color: #161b22;
+    border-radius: 6px;
+    padding: 10px 14px;
+    width: 24%;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.3);
+    display: flex;
+    flex-direction: column;
+}
+.kpi-symbol {
+    font-weight: 700;
+    font-size: 15px;
+    color: #f0f6fc;
+}
+.kpi-price {
+    font-size: 20px;
+    font-weight: 600;
+    color: #f9fafb;
+}
+.kpi-change {
+    font-size: 13px;
+    margin-top: 2px;
+}
+
+/* === Chart Embed (ECharts + Highcharts) === */
+[data-testid="html"] {
+    width: 100% !important;
+    background: #0d1117 !important;
+    border: 1px solid #30363d !important;
+    border-radius: 6px !important;
+    overflow: hidden !important;
+}
+
+/* === Remove Gray Placeholder Icons === */
+[data-testid="plot-container"] svg {
+    display: none !important;
+}
+[data-testid="plot-container"] .wrap {
+    background: transparent !important;
+    box-shadow: none !important;
+}
+
+/* === Plot Layout === */
+[data-testid="plot-container"] {
+    width: 100% !important;
+    margin: 0 auto 16px auto !important;
+}
+[data-testid="plot-container"] canvas {
+    width: 100% !important;
+    height: auto !important;
+}
+
+/* === Chart Heights === */
+#root [label="Market Composition"] canvas { height: 360px !important; }
+#root [label="Top Movers"] canvas,
+#root [label="Market Cap vs Volume"] canvas { height: 320px !important; }
+
+/* === Sidebar === */
+#ai_summary_sidebar textarea {
+    height: 360px !important;
+    background-color: #161b22 !important;
+    color: #f0f6fc !important;
+    border: 1px solid #30363d !important;
+    border-radius: 6px !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    font-size: 13.5px !important;
+    line-height: 1.5 !important;
+    padding: 12px !important;
+    resize: none !important;
+}
+
+/* === Controls === */
+.gr-button {
+    border-radius: 6px !important;
+    font-weight: 600 !important;
+    letter-spacing: 0.3px;
+    height: 52px !important;
+    background: linear-gradient(90deg, #4f46e5, #6366f1) !important;
+    border: none !important;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.25);
+    transition: all 0.2s ease-in-out;
+}
+.gr-button:hover {
+    filter: brightness(1.08);
+    transform: translateY(-1px);
+}
+.gr-slider {
+    height: 52px !important;
+}
+.gr-slider input[type=range]::-webkit-slider-thumb {
+    background: #6366f1 !important;
+}
+
+/* === Table / Row spacing === */
+.gr-dataframe table {
+    width: 100% !important;
+    color: #c9d1d9 !important;
+    background: #161b22 !important;
+}
+.gr-dataframe th {
+    background-color: #21262d !important;
+    color: #f0f6fc !important;
+    border-bottom: 1px solid #30363d !important;
+    text-transform: uppercase;
+    font-weight: 600 !important;
+}
+.gr-dataframe td {
+    border-top: 1px solid #30363d !important;
+    padding: 8px !important;
+}
+.gr-row { gap: 16px !important; }
+

core/visual_comparison.py

@@ -0,0 +1,94 @@
+"""
+Module: visual_comparison.py
+Purpose: Interactive crypto pair comparison (Plotly + CoinGecko)
+"""
+
+import requests
+import pandas as pd
+import plotly.graph_objects as go
+
+COINGECKO_API = "https://api.coingecko.com/api/v3"
+
+
+def get_coin_history(coin_id: str, days: int = 180):
+    """Fetch historical market data for given coin from CoinGecko API."""
+    url = f"{COINGECKO_API}/coins/{coin_id}/market_chart?vs_currency=usd&days={days}"
+    r = requests.get(url)
+    r.raise_for_status()
+    data = r.json()
+    df = pd.DataFrame(data["prices"], columns=["timestamp", "price"])
+    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
+    return df
+
+
+def build_price_chart(pair: tuple[str, str], days: int = 180):
+    """Build comparative price chart for selected pair."""
+    coin_a, coin_b = pair
+
+    df_a = get_coin_history(coin_a, days)
+    df_b = get_coin_history(coin_b, days)
+
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=df_a["timestamp"],
+        y=df_a["price"],
+        name=f"{coin_a.capitalize()} / USD",
+        line=dict(width=2),
+    ))
+    fig.add_trace(go.Scatter(
+        x=df_b["timestamp"],
+        y=df_b["price"],
+        name=f"{coin_b.capitalize()} / USD",
+        line=dict(width=2),
+    ))
+
+    fig.update_layout(
+        template="plotly_dark",
+        height=480,
+        margin=dict(l=40, r=20, t=30, b=40),
+        xaxis_title="Date",
+        yaxis_title="Price (USD)",
+        legend_title="Asset",
+        hovermode="x unified",
+    )
+
+    return fig
+
+
+def build_volatility_chart(pair: tuple[str, str], days: int = 180):
+    """Build comparative volatility chart for selected pair."""
+    coin_a, coin_b = pair
+
+    df_a = get_coin_history(coin_a, days)
+    df_b = get_coin_history(coin_b, days)
+
+    df_a["returns"] = df_a["price"].pct_change() * 100
+    df_b["returns"] = df_b["price"].pct_change() * 100
+
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=df_a["timestamp"],
+        y=df_a["returns"],
+        name=f"{coin_a.upper()} Daily Change (%)",
+        mode="lines",
+        line=dict(width=1.6),
+    ))
+    fig.add_trace(go.Scatter(
+        x=df_b["timestamp"],
+        y=df_b["returns"],
+        name=f"{coin_b.upper()} Daily Change (%)",
+        mode="lines",
+        line=dict(width=1.6),
+    ))
+
+    fig.update_layout(
+        template="plotly_dark",
+        height=400,
+        margin=dict(l=40, r=20, t=30, b=40),
+        xaxis_title="Date",
+        yaxis_title="Daily Change (%)",
+        legend_title="Volatility",
+        hovermode="x unified",
+    )
+
+    return fig

core/visualization.py

@@ -0,0 +1,58 @@
+"""
+🇬🇧 Module: visualization.py
+Purpose: Contains visualization utilities such as plotting charts for portfolio analytics (e.g., Alpha vs BTC).
+
+🇷🇺 Модуль: visualization.py
+Назначение: содержит функции визуализации данных, включая построение графиков портфелей (например, Alpha к BTC).
+"""
+
+import matplotlib.pyplot as plt
+import requests
+from typing import Optional
+from config import EXTERNAL_API_URL, DEBUG
+
+
+def build_alpha_chart(portfolio_id: str) -> Optional[plt.Figure]:
+    """Fetch alphaBTC series and build a matplotlib figure."""
+    url = (
+        f"{EXTERNAL_API_URL}/portfolio/get?"
+        f"portfolioId={portfolio_id}&extended=1&declaration=1&step=day&lang=en&incViews=1"
+    )
+    headers = {"User-Agent": "Mozilla/5.0", "Accept": "application/json"}
+
+    try:
+        response = requests.get(url, headers=headers)
+        if DEBUG:
+            print(f"[DEBUG] GET {url} -> {response.status_code}")
+            print(f"[DEBUG] Response preview: {response.text[:200]}...")
+
+        response.raise_for_status()
+        data = response.json()
+        extended = data.get("data", {}).get("extended", {})
+        alpha_data = extended.get("alphaBTC", [])
+
+        if not alpha_data or not isinstance(alpha_data, list):
+            print("[WARN] No alphaBTC data found.")
+            return None
+
+        values = [item.get("value", 0) for item in alpha_data]
+        indices = list(range(len(values)))
+
+        # Create plot
+        fig, ax = plt.subplots(figsize=(12, 6))
+        ax.plot(indices, values, color="blue", label="alphaBTC")
+        ax.axhline(0, color="black", linewidth=1)
+        ax.set_title("Alpha vs BTC", fontsize=14)
+        ax.set_xlabel("Index")
+        ax.set_ylabel("Alpha")
+        ax.grid(True, linestyle="--", alpha=0.5)
+        ax.legend()
+        plt.tight_layout()
+
+        if DEBUG:
+            print("[DEBUG] Chart built successfully ✅")
+        return fig
+
+    except Exception as e:
+        print(f"[ERROR] Exception while building chart: {e}")
+        return None

prompts/__init__.py

@@ -0,0 +1,2 @@
+# __init__.py
+# Marks this directory as a Python package.

prompts/reference_templates.py

@@ -0,0 +1,39 @@
+"""
+🇬🇧 Module: reference_templates.py
+Purpose: Defines reusable prompt templates for portfolio analysis and comparison.
+
+🇷🇺 Модуль: reference_templates.py
+Назначение: содержит шаблоны промптов для анализа и сравнения портфелей.
+"""
+
+REFERENCE_PROMPT = (
+    "**Analysis**\n\n"
+    "For each key metric, provide value and brief interpretation:\n"
+    "- Annualized Return: {value1}\n"
+    "- Max Drawdown: {value2}\n"
+    "- Sharpe Ratio: {value3}\n"
+    "- Sortino Ratio: {value4}\n"
+    "- Calmar Ratio: {value5}\n"
+    "- Beta: {value6}\n"
+    "- Volatility: {value7}\n\n"
+    "**Interpretation**\n"
+    "Summarize the overall strategy characteristics, risk level, and stability.\n\n"
+    "**Recommendation**\n"
+    "Provide a brief conclusion about the portfolio’s viability."
+)
+
+REFERENCE_COMPARISON_PROMPT = (
+    "**Comparison**\n\n"
+    "Portfolio A:\n"
+    "- Annualized Return: {a1}\n"
+    "- Max Drawdown: {a2}\n"
+    "- Sharpe Ratio: {a3}\n"
+    "- Sortino Ratio: {a4}\n\n"
+    "Portfolio B:\n"
+    "- Annualized Return: {b1}\n"
+    "- Max Drawdown: {b2}\n"
+    "- Sharpe Ratio: {b3}\n"
+    "- Sortino Ratio: {b4}\n\n"
+    "**Summary**\n"
+    "Highlight main differences, strengths, and investor suitability."
+)

prompts/system_prompts.py

@@ -0,0 +1,54 @@
+"""
+🇬🇧 Module: system_prompts.py
+Purpose: Stores system-level instructions for LLM.
+
+🇷🇺 Модуль: system_prompts.py
+Назначение: хранит системные инструкции для LLM.
+"""
+
+ANALYSIS_SYSTEM_PROMPT = """
+You are a quantitative portfolio analyst.
+
+Given a single portfolio’s metrics, provide:
+1️⃣ **Objective evaluation** of performance (returns, alpha, Sharpe ratio).
+2️⃣ **Risk assessment** (volatility, max drawdown).
+3️⃣ **Interpretation of efficiency** (how well risk and return balance).
+4️⃣ **Concise recommendation** — state whether the portfolio is performing *well, neutrally, or poorly*, and what could be improved.
+
+Avoid general statements. Use data context.
+Your tone: professional, precise, analytical.
+"""
+
+COMPARISON_SYSTEM_PROMPT = """
+
+You are a financial analysis model specializing in quantitative portfolio comparison.
+
+Your goal is to compare two portfolios **objectively** using their metrics.
+
+When analyzing:
+- Do not make emotional or vague statements.
+- Base conclusions strictly on the provided numerical data.
+- Identify *which portfolio is statistically stronger* based on performance, risk, and stability.
+
+Your output must be concise and structured in the following format:
+
+Comparative Analysis
+- Discuss which portfolio shows **better performance** (higher return / alpha).
+- Discuss **risk-adjusted efficiency** (Sharpe / Sortino ratio, volatility, maxDD).
+- Highlight **consistency** and **downside protection** if metrics imply it.
+
+Recommendation
+Provide an actionable insight:
+- If one portfolio is clearly better, state which one and why.
+- If both are weak or over-risked — say it explicitly.
+- If data is incomplete or contradictory — mention this clearly.
+
+Avoid phrases like “depends on the investor” or “might be better for risk-taking investors”.
+You are the quantitative analyst — give a judgment based on data, not preference.
+"""
+
+
+GENERAL_CONTEXT = (
+    "You are an intelligent assistant specializing in investment portfolios, finance, "
+    "and market analysis. Answer clearly, concisely, and professionally."
+)

requirements.txt

@@ -0,0 +1,12 @@
+gradio>=4.29.0
+openai>=1.30.1
+requests
+httpx
+pandas
+matplotlib
+plotly
+yfinance>=0.2.43
+plotly>=6.3.1
+altair
+pyecharts
+jinja2

services/__init__.py

@@ -0,0 +1,2 @@
+# __init__.py
+# Marks this directory as a Python package.

services/llm_client.py

@@ -0,0 +1,39 @@
+"""
+🇬🇧 Module: llm_client.py
+Purpose: Adapter for Featherless.ai (OpenAI-compatible API).
+
+🇷🇺 Модуль: llm_client.py
+Назначение: адаптер для LLM-инференса через Featherless.ai (совместимо с OpenAI API).
+"""
+
+import os
+from typing import List, Dict, Generator
+from openai import OpenAI
+from config import FEATHERLESS_API_KEY, FEATHERLESS_MODEL
+
+
+class FeatherlessLLM:
+    """Wrapper for Featherless.ai LLM inference."""
+
+    def __init__(self, api_key: str = FEATHERLESS_API_KEY, model: str = FEATHERLESS_MODEL):
+        if not api_key:
+            raise RuntimeError("❌ Environment variable 'featherless' (API key) is missing.")
+        self.client = OpenAI(base_url="https://api.featherless.ai/v1", api_key=api_key)
+        self.model = model
+
+    def stream_chat(self, *, messages: List[Dict], model: str = None) -> Generator[str, None, None]:
+        """Stream chat completion using Featherless.ai."""
+        used_model = model or self.model
+        response = self.client.chat.completions.create(
+            model=used_model,
+            messages=messages,
+            stream=True,
+        )
+        for chunk in response:
+            delta = chunk.choices[0].delta.content
+            if delta:
+                yield delta
+
+
+# === Global singleton instance ===
+llm_service = FeatherlessLLM()

services/output_api.py

@@ -0,0 +1,76 @@
+"""
+🇬🇧 Module: api_client.py
+Purpose: Provides async API client for external portfolio analytics service.
+Handles fetching metrics, alphaBTC data, and other portfolio information.
+
+🇷🇺 Модуль: api_client.py
+Назначение: асинхронный клиент для внешнего API аналитики портфелей,
+выполняющий запросы к метрикам, данным alphaBTC и другой информации о портфелях.
+"""
+
+import re
+import httpx
+from typing import Any, Dict, List, Optional
+from config import EXTERNAL_API_URL, REQUEST_TIMEOUT, DEBUG
+
+# === UUID detection ===
+UUID_PATTERN = re.compile(
+    r"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}"
+)
+
+
+def extract_portfolio_id(text: str) -> Optional[str]:
+    """Extract a portfolio UUID from text or URL."""
+    match = UUID_PATTERN.search(text or "")
+    return match.group(0) if match else None
+
+
+async def _get_json(url: str) -> Dict[str, Any]:
+    """Generic helper to send GET request and parse JSON."""
+    if DEBUG:
+        print(f"[DEBUG] Requesting URL: {url}")
+
+    async with httpx.AsyncClient(timeout=REQUEST_TIMEOUT) as client:
+        r = await client.get(url, headers={"User-Agent": "Mozilla/5.0", "Accept": "application/json"})
+        r.raise_for_status()
+        return r.json()
+
+
+async def fetch_metrics_async(portfolio_id: str) -> Optional[Dict[str, Any]]:
+    """Fetch portfolio metrics (extended data)."""
+    url = f"{EXTERNAL_API_URL}/portfolio/get?portfolioId={portfolio_id}&extended=1"
+    try:
+        data = await _get_json(url)
+        extended = data.get("data", {}).get("extended", {})
+        result = {}
+        for k, v in extended.items():
+            if isinstance(v, (int, float)):
+                # Convert some fields to percentages for readability
+                if k in {"cagr", "alphaRatio", "volatility", "maxDD"}:
+                    result[k] = v * 100
+                else:
+                    result[k] = v
+        if DEBUG:
+            print(f"[DEBUG] Metrics fetched for {portfolio_id}: {result}")
+        return result
+    except Exception as e:
+        if DEBUG:
+            print(f"[ERROR] fetch_metrics_async: {e}")
+        return None
+
+
+async def fetch_absolute_pnl_async(portfolio_id: str) -> Optional[List[Dict[str, Any]]]:
+    """Fetch absolutePnL daily data."""
+    url = f"{EXTERNAL_API_URL}/portfolio/get?portfolioId={portfolio_id}&extended=1&step=day"
+    try:
+        data = await _get_json(url)
+        pnl = data.get("data", {}).get("extended", {}).get("absolutePnL", [])
+        if not isinstance(pnl, list):
+            if DEBUG:
+                print("[ERROR] absolutePnL is not a list or missing")
+            return None
+        return pnl
+    except Exception as e:
+        if DEBUG:
+            print(f"[ERROR] fetch_absolute_pnl_async: {e}")
+        return None