report Agent dockerfile and environment secrets are generated from an entry_point.sh file that will also obtain the secrets from HF Spaces.

Dockerfile

@@ -5,7 +5,7 @@
 ########################
 FROM python:3.11-slim AS fetcher
 
-ARG GIT_REPO="https://github.com/zBotta/reportingAgent.git"
+ARG GIT_REPO=https://github.com/zBotta/reportingAgent.git
 # TODO: after tests change GIT_REF to main
 ARG GIT_REF=dev   # branch or tag (for a specific commit, see notes below) 
 
@@ -46,18 +46,16 @@ RUN useradd -m appuser
 WORKDIR /reportAgent
 
 # Install Python deps first for better layer caching
-# (requirements.txt is at root/)
-COPY src/requirements.txt reportAgent/requirements.txt
+COPY --from=fetcher src/requirements.txt /reportAgent/requirements.txt
 RUN pip install -r requirements.txt
 
-COPY src/projectSetup.py /reportAgent/
-COPY src/entry_point.sh /reportAgent/
-RUN chmod +x /entrypoint.sh && chown -R appuser:appuser /reportAgent /entrypoint.sh /home/appuser
-USER appuser
-
+COPY --from=fetcher src/projectSetup.py /reportAgent/projectSetup.py
+COPY --from=fetcher src/entry_point.sh /reportAgent/entry_point.sh
 # Copy your app code (root/app -> /app)
-COPY src/reportAgent/app/ reportAgent/app/
-RUN chown -R appuser:appuser reportAgent/ /home/appuser
+COPY --from=fetcher src/app/ /reportAgent/app/
+
+RUN chmod +x /reportAgent/entry_point.sh && chown -R appuser:appuser /reportAgent /reportAgent/entry_point.sh /home/appuser
+RUN chown -R appuser:appuser /reportAgent/ /home/appuser
 
 # Drop root
 USER appuser

README.md

@@ -12,9 +12,15 @@ short_description: A LM agent that creates reports
 license: mit
 ---
 
-# Welcome to Streamlit!
+# AI Reporting Agent
+An AI reporting agent that uses a Language Model to generate reports
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+# Description
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+In many industries, as part of a highly performing Quality Assurance and Customer Support functions, reporting unusual events, anomalies, or process deviations requires clear, structured documentation that captures the full context: what happened, when, where, who was involved, why it occurred, and what actions were taken… This project proposes the development of a chatbot assistant that interacts with the user to collect all relevant information through a series of structured questions. The assistant then automatically generates a well-written, standardized report based on the input.
+
+# UI
+
+In the presented UI, the user can charge the models that we have selected after our research.
+In order to generate reports, the user fills a form and press the generate button.
+The report is shown in the UI and can be downloaded as a pdf file.

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])),
-    ))