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Dockerfile

@@ -1,21 +1,13 @@
-FROM python:3.9-slim
-
+FROM python:3.10-slim
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
 WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+COPY requirements.txt /app/
+RUN pip install --upgrade pip && pip install -r requirements.txt
+COPY . /app
+ENV STREAMLIT_PORT 7860
+ENV STREAMLIT_HEADLESS true
+ENV STREAMLIT_ENABLECORS false
+ENV STREAMLIT_SERVER_ENABLE_WEBRTC false
+EXPOSE 7860
+CMD ["streamlit", "run", "app.py"]

README.md

@@ -1,19 +1,16 @@
----
-title: BizIntel AI
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
----
+# 🚀 BizIntel AI Ultra
 
-# Welcome to Streamlit!
+**Next-gen Business Analytics App powered by Google ADK, Gemini Pro, and Streamlit**
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+## Features
+- Upload CSV or connect to databases (PostgreSQL, MySQL, MSSQL, Oracle, SQLite)
+- ADK multi-agent orchestration: trend analysis, forecasting, strategic insights
+- Interactive Streamlit dashboard with visual charts
+- Hugging Face Spaces Docker-based deployment
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## Usage
+1. Select your data source: file upload or SQL DB connection
+2. Run agent analysis pipeline
+3. View KPIs, charts, forecasts, and strategy
+
+Built with ❤️ using Streamlit + Gemini + ADK

app.py

@@ -0,0 +1,41 @@
+
+import streamlit as st
+from agents.analytics_pipeline import analytics_coordinator
+import os
+from db_connector import fetch_data_from_db, list_tables, SUPPORTED_ENGINES
+
+st.set_page_config(page_title="BizIntel AI Ultra", layout="wide")
+st.title("📊 BizIntel AI Ultra - Ultimate Business Intelligence")
+
+input_source = st.radio("Select data source", ["Upload CSV", "Connect to SQL Database"])
+file_path = None
+
+if input_source == "Upload CSV":
+    uploaded_file = st.file_uploader("Upload CSV", type="csv")
+    if uploaded_file:
+        file_path = os.path.join("data", uploaded_file.name)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.read())
+        st.success("File uploaded.")
+
+elif input_source == "Connect to SQL Database":
+    engine = st.selectbox("Select database engine", SUPPORTED_ENGINES)
+    conn_str = st.text_input("Connection string (SQLAlchemy format)")
+    if conn_str:
+        tables = list_tables(conn_str)
+        if tables:
+            table_name = st.selectbox("Choose a table", tables)
+            if table_name:
+                file_path = fetch_data_from_db(conn_str, table_name)
+                st.success(f"Fetched table '{table_name}' as CSV.")
+
+if file_path:
+    st.info("Running analytics pipeline...")
+    result = analytics_coordinator.run(input=file_path)
+    st.subheader("Analysis & Strategy Report")
+    st.text(result)
+
+    if os.path.exists("sales_plot.png"):
+        st.image("sales_plot.png", caption="Sales Trend", use_column_width=True)
+    if os.path.exists("forecast_plot.png"):
+        st.image("forecast_plot.png", caption="Forecast Chart", use_column_width=True)

db_connector.py

@@ -0,0 +1,17 @@
+
+import pandas as pd
+from sqlalchemy import create_engine, inspect
+
+SUPPORTED_ENGINES = ["SQLite", "PostgreSQL", "MySQL", "MSSQL", "Oracle"]
+
+def list_tables(conn_str):
+    engine = create_engine(conn_str)
+    inspector = inspect(engine)
+    return inspector.get_table_names()
+
+def fetch_data_from_db(conn_str, table):
+    engine = create_engine(conn_str)
+    df = pd.read_sql_table(table, engine)
+    csv_path = f"data/{table}_extracted.csv"
+    df.to_csv(csv_path, index=False)
+    return csv_path

requirements.txt

@@ -1,3 +1,11 @@
-altair
+google-adk
 pandas
-streamlit
+matplotlib
+statsmodels
+streamlit
+plotly
+sqlalchemy
+psycopg2-binary
+mysql-connector-python
+pyodbc
+cx_Oracle