on my sys

.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
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.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
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,20 +1,20 @@
-# Use the official Python 3.9 image
-FROM python:3.9-slim
+FROM python:3.13.5-slim
 
-# Set the working directory
-WORKDIR /code
+WORKDIR /app
 
-# Copy the requirements file
-COPY ./requirements.txt /code/requirements.txt
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
 
-# Install the dependencies
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+COPY requirements.txt ./
+COPY src/ ./src/
 
-# Copy the rest of the application code
-COPY . /code
+RUN pip3 install -r requirements.txt
 
-# Expose the default Streamlit port
 EXPOSE 8501
 
-# Command to run the application
-CMD ["streamlit", "run", "app.py", "--server.address", "0.0.0.0", "--server.port", "7860"]
+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"]

README.md

@@ -1,17 +1,20 @@
 ---
 title: Sentiment Analysis App
-emoji: 🤖
-colorFrom: blue
-colorTo: indigo
+emoji: 🚀
+colorFrom: red
+colorTo: red
 sdk: docker
-# sdk_version: 1.28.0 # Not needed for docker
-app_port: 7860
-app_file: app.py
+app_port: 8501
+tags:
+- streamlit
 pinned: false
+short_description: Sentiment Analysis App
+license: mit
 ---
 
-# Sentiment Analysis App
+# Welcome to Streamlit!
 
-This is a simple Sentiment Analysis application built with [Streamlit](https://streamlit.io) and [Hugging Face Transformers](https://huggingface.co/docs/transformers).
+Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 
-It uses the default `distilbert-base-uncased-finetuned-sst-2-english` model to classify text as **POSITIVE** or **NEGATIVE**.
+If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
+forums](https://discuss.streamlit.io).

app.py

@@ -1,36 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-# Set page title and header
-st.set_page_config(page_title="Sentiment Analysis App", page_icon="🤖")
-st.title("🤖 Sentiment Analysis with Hugging Face")
-
-st.markdown("""
-This app uses a pre-trained machine learning model from Hugging Face Transformers to analyze the sentiment of your text.
-""")
-
-# Load the pipeline (cached to avoid reloading on every interaction)
-@st.cache_resource
-def load_sentiment_pipeline():
-    return pipeline("sentiment-analysis")
-
-classifier = load_sentiment_pipeline()
-
-# User input
-text_input = st.text_area("Enter some text here:", height=150, placeholder="I love building cool AI apps!")
-
-if st.button("Analyze Sentiment"):
-    if text_input.strip():
-        with st.spinner("Analyzing..."):
-            result = classifier(text_input)[0]
-            label = result['label']
-            score = result['score']
-            
-            if label == 'POSITIVE':
-                st.success(f"**Sentiment:** {label} 😊")
-            else:
-                st.error(f"**Sentiment:** {label} 😔")
-            
-            st.metric("Confidence Score", f"{score:.4f}")
-    else:
-        st.warning("Please enter some text to analyze.")

requirements.txt

@@ -1,3 +1,3 @@
-streamlit
-transformers
-torch
+altair
+pandas
+streamlit

src/streamlit_app.py

@@ -0,0 +1,40 @@
+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])),
+    ))