Updated Dockerfile

Dockerfile

@@ -1,3 +1,60 @@
+###############################################################################
+# Base image – pick slim Python for a smaller footprint
+###############################################################################
+FROM python:3.10-slim
+
+# --------------------------------------------------------------------------- #
+# 1. General runtime settings                                                 #
+# --------------------------------------------------------------------------- #
+ENV PYTHONUNBUFFERED=1
+ENV PIP_NO_CACHE_DIR=1
+ENV HF_HOME=/hf
+WORKDIR /app
+
+# --------------------------------------------------------------------------- #
+# 2. System packages often required by ML / CV libs & Streamlit front-ends    #
+# --------------------------------------------------------------------------- #
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        git git-lfs            \  # for pulling large LFS blobs / models
+        ffmpeg libsm6 libxext6 \  # common for OpenCV / video / image ops
+    && rm -rf /var/lib/apt/lists/* \
+    && git lfs install
+
+# --------------------------------------------------------------------------- #
+# 3. Python deps                                                              #
+# --------------------------------------------------------------------------- #
+# a) First install *only* requirements.txt to leverage Docker layer caching
+COPY requirements.txt .
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+# --------------------------------------------------------------------------- #
+# 4. Project source                                                           #
+# --------------------------------------------------------------------------- #
+# Copy each top-level directory you showed in the screenshot
+COPY --link --chown=1000:1000 ./frontend ./frontend
+COPY --link --chown=1000:1000 ./data ./data
+COPY --link --chown=1000:1000 ./insucompass ./insucompass
+COPY --link --chown=1000:1000 ./scripts ./scripts
+
+# If you need your SQLite DB, .env, etc. at runtime copy them, too
+COPY insucompass.db .
+
+# --------------------------------------------------------------------------- #
+# 5. Ports & startup command                                                  #
+# --------------------------------------------------------------------------- #
+ENV PORT=7860
+EXPOSE $PORT
+
+# FastAPI app entry-point assumed at  insucompass/main.py → app = FastAPI(...)
+# Adjust the dotted path if your `FastAPI()` object lives elsewhere.
+CMD ["uvicorn", "insucompass.main:app", "--host", "0.0.0.0", "--port", "7860"]
+
+
+
+
+
+
 ###############################################################################
 # ---------------------------  Stage 1: base image  -------------------------- #
 ###############################################################################
@@ -31,8 +88,7 @@ RUN --mount=target=/app/requirements.txt,source=requirements.txt \
 # --- Copy Application Code ---
 # Copy all our application code into the container.
 # Using --link can speed up builds on filesystems that support it.
-COPY --link --chown=1000:1000 ./insucompass ./insucompass
-COPY --link --chown=1000:1000 ./scripts ./scripts
+
 
 # --- Create Writable Data Directory ---
 # This is crucial for ChromaDB and the LangGraph checkpointer.

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))