Push to 4.0.2 and with tgb

src/.python-version

@@ -0,0 +1 @@
+3.11

src/main.py

@@ -145,4 +145,4 @@ def analyze_file(state) -> None:
 }
 
 
-Gui(pages=pages).run(title="Sentiment Analysis")
+Gui(pages=pages).run(title="Sentiment Analysis", dark_mode=True)

src/main_tgb.py

@@ -0,0 +1,169 @@
+import numpy as np
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from scipy.special import softmax
+
+import taipy.gui.builder as tgb
+from taipy.gui import notify, Gui
+
+# ----------------------------------
+# Model & Data Initialization
+# ----------------------------------
+MODEL = "sbcBI/sentiment_analysis_model"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+
+text = "Original text"
+dataframe = pd.DataFrame(
+    {
+        "Text": [""],
+        "Score Pos": [0.33],
+        "Score Neu": [0.33],
+        "Score Neg": [0.33],
+        "Overall": [0],
+    }
+)
+dataframe2 = dataframe.copy()
+
+path = ""
+treatment = 0
+
+# ----------------------------------
+# Helper Functions
+# ----------------------------------
+def analyze_text(input_text: str) -> dict:
+    """
+    Runs the sentiment analysis model on the text.
+    Returns a dictionary with the scores.
+    """
+    encoded_text = tokenizer(input_text, return_tensors="pt")
+    output = model(**encoded_text)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+
+    return {
+        "Text": input_text[:50],
+        "Score Pos": scores[2],
+        "Score Neu": scores[1],
+        "Score Neg": scores[0],
+        "Overall": scores[2] - scores[0],
+    }
+
+
+def local_callback(state):
+    """
+    Analyze the text and update the main dataframe.
+    """
+    notify(state, "Info", f"The text is: {state.text}", True)
+    temp = state.dataframe.copy()
+    scores = analyze_text(state.text)
+    temp.loc[len(temp)] = scores
+    state.dataframe = temp
+    state.text = ""
+
+
+def analyze_file(state):
+    """
+    Analyze each line of the uploaded text file and update `dataframe2`.
+    """
+    state.dataframe2 = dataframe2
+    state.treatment = 0
+
+    with open(state.path, "r", encoding="utf-8") as f:
+        data = f.read()
+        file_list = data.split("\n")
+
+    for i, input_text in enumerate(file_list):
+        state.treatment = int((i + 1) * 100 / len(file_list))
+        temp = state.dataframe2.copy()
+        scores = analyze_text(input_text)
+        temp.loc[len(temp)] = scores
+        state.dataframe2 = temp
+
+    state.path = None
+
+
+# ----------------------------------
+# Building Pages with TGB
+# ----------------------------------
+
+# ---------------------
+# Home Page ("/")
+# ---------------------
+with tgb.Page() as root_page:
+    tgb.toggle(theme=True)
+    tgb.navbar()
+
+# ---------------------
+# "line" Page
+# ---------------------
+with tgb.Page() as page:
+    tgb.text("# Getting started with **Taipy** GUI", mode="md")
+
+    # Layout with two columns
+    tgb.text("**My text:** {text}", mode="md")
+    tgb.input("{text}", label="Enter a word:")
+    tgb.button("Analyze", on_action=local_callback)
+
+    # Display the main dataframe as a table
+    tgb.text("### Analyzed Entries", mode="md")
+    tgb.table("{dataframe}", number_format="%.2f")
+
+    # Summaries in a 1-1-1 layout
+    with tgb.layout(columns="1 1 1"):
+        tgb.text(lambda dataframe: f"## Positive {np.mean(dataframe['Score Pos']):.2f}", class_name="h4")
+
+        tgb.text(lambda dataframe: f" ## Neutral{np.mean(dataframe['Score Neu']):.2f}", class_name="h4")
+
+        tgb.text(lambda dataframe: f" ## Negative{np.mean(dataframe['Score Neg']):.2f}", class_name="h4")
+
+    # Bar + line chart
+    tgb.chart(
+        "{dataframe}",
+        x="Text",
+        y=["Score Pos", "Score Neu", "Score Neg", "Overall"],
+        color=["green", "grey", "red", "yellow"],
+        type=["bar", "bar", "bar", "line"],  # or pass a list for 'type'
+        title="Sentiment Trends",
+    )
+
+# ---------------------
+# "text" Page
+# ---------------------
+with tgb.Page() as page_file:
+    tgb.text("## File Uploader", mode="md")
+    # File selector & progress text
+    tgb.file_selector(
+        "{path}",
+        label="Upload .txt file",
+        extensions=".txt",
+        on_action=analyze_file
+    )
+    tgb.text(lambda treatment: f"Downloading {treatment}%...", mode="md")
+
+    tgb.text("### Analyzed Entries from File", mode="md")
+    tgb.table("{dataframe2}", number_format="%.2f")
+
+    # Bar + line chart for file-based results
+    tgb.chart(
+        "{dataframe2}",
+        type="bar",
+        x="Text",
+        y=["Score Pos", "Score Neu", "Score Neg", "Overall"],
+        color=["green", "grey", "red", None],
+        subtypes={"Overall": "line"},
+        title="Sentiment from File",
+        height="600px",
+    )
+
+# ---------------------
+# Run the App
+# ---------------------
+pages = {
+    "/": root_page,
+    "line": page,
+    "text": page_file,
+}
+gui = Gui(pages=pages)
+gui.run(title="Sentiment Analysis", dark_mode=True)
+

src/pyproject.toml

@@ -0,0 +1,12 @@
+[project]
+name = "src"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "scipy>=1.15.1",
+    "taipy>=4.0.2",
+    "torch>=2.5.1",
+    "transformers>=4.48.1",
+]