Update app.py
Browse files
app.py
CHANGED
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@@ -6,49 +6,87 @@ import gradio as gr
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from analysis_core import extract_chats, get_chat_name, analyze_chat
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# -------------------- THEME --------------------
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THEME = gr.themes.Soft(
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primary_hue="fuchsia",
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secondary_hue="pink",
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neutral_hue="slate",
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)
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CSS = """
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/*
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.gradio-container {
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max-width: 100% !important;
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padding-left: 24px !important;
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padding-right: 24px !important;
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}
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/*
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.btn-load button {
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background: linear-gradient(90deg, #d946ef, #ec4899) !important;
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border-radius: 16px !important;
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font-weight: 700 !important;
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}
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/* Analyze button (green, very clear action) */
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.btn-analyze button {
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background: linear-gradient(90deg, #22c55e, #16a34a) !important;
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border-radius: 16px !important;
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font-weight: 700 !important;
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}
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/*
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.status-box textarea {
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border-radius: 14px !important;
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font-weight: 500;
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}
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/*
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.plot-container {
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}
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"""
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# -------------------- HELPERS --------------------
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def _path(file_obj) -> str:
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if file_obj is None:
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raise gr.Error("upload Telegram result.json first.")
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@@ -103,7 +141,6 @@ def run_analysis(choice: str, state: Dict[str, Any], max_bert_persian: int):
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return result, fig, pos_rows, neg_rows
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# -------------------- UI --------------------
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with gr.Blocks(
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title="Telegram Sentiment Analysis",
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theme=THEME,
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@@ -142,15 +179,8 @@ with gr.Blocks(
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label="Max Persian messages to run BERT on (speed control)",
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)
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load_btn = gr.Button(
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elem_classes=["btn-load"],
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)
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analyze_btn = gr.Button(
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"Analyze selected chat",
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elem_classes=["btn-analyze"],
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)
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out_json = gr.JSON(label="Results (JSON)")
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@@ -162,11 +192,13 @@ with gr.Blocks(
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out_pos = gr.Dataframe(
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label="Top 5 positive lex words (word, score)",
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headers=["word", "score"],
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)
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out_neg = gr.Dataframe(
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label="Top 5 negative lex words (word, score)",
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headers=["word", "score"],
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)
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load_btn.click(load_chats, inputs=[file_in], outputs=[chat_dd, state, status])
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from analysis_core import extract_chats, get_chat_name, analyze_chat
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THEME = gr.themes.Soft(
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primary_hue="fuchsia",
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secondary_hue="pink",
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neutral_hue="slate",
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)
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CSS = """
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/* full width */
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.gradio-container {
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max-width: 100% !important;
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padding-left: 24px !important;
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padding-right: 24px !important;
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}
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/* buttons */
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.btn-load button {
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background: linear-gradient(90deg, #d946ef, #ec4899) !important;
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border-radius: 16px !important;
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font-weight: 700 !important;
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}
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.btn-analyze button {
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background: linear-gradient(90deg, #22c55e, #16a34a) !important;
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border-radius: 16px !important;
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font-weight: 700 !important;
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}
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/* status */
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.status-box textarea {
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border-radius: 14px !important;
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font-weight: 500;
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}
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/* plot */
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.plot-container { min-height: 520px; }
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/* -------- Dataframe styling (match magenta theme) -------- */
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.lex-table .wrap {
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border-radius: 16px !important;
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border: 1px solid rgba(236,72,153,0.28) !important;
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overflow: hidden !important;
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}
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.lex-table table {
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border-collapse: separate !important;
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border-spacing: 0 !important;
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}
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/* header */
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.lex-table thead th {
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background: linear-gradient(90deg, rgba(217,70,239,0.35), rgba(236,72,153,0.25)) !important;
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color: rgba(255,255,255,0.92) !important;
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font-weight: 800 !important;
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border-bottom: 1px solid rgba(236,72,153,0.28) !important;
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}
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/* body cells */
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.lex-table tbody td {
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background: rgba(255,255,255,0.02) !important;
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border-bottom: 1px solid rgba(236,72,153,0.10) !important;
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}
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/* zebra rows */
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.lex-table tbody tr:nth-child(even) td {
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background: rgba(217,70,239,0.06) !important;
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}
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/* hover */
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.lex-table tbody tr:hover td {
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background: rgba(236,72,153,0.14) !important;
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}
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/* align */
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.lex-table td, .lex-table th { padding: 10px 12px !important; }
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/* make numbers a bit clearer */
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.lex-table td:last-child {
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font-variant-numeric: tabular-nums;
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}
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"""
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def _path(file_obj) -> str:
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if file_obj is None:
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raise gr.Error("upload Telegram result.json first.")
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return result, fig, pos_rows, neg_rows
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with gr.Blocks(
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title="Telegram Sentiment Analysis",
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theme=THEME,
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label="Max Persian messages to run BERT on (speed control)",
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)
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load_btn = gr.Button("Load chats", elem_classes=["btn-load"])
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analyze_btn = gr.Button("Analyze selected chat", elem_classes=["btn-analyze"])
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out_json = gr.JSON(label="Results (JSON)")
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out_pos = gr.Dataframe(
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label="Top 5 positive lex words (word, score)",
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headers=["word", "score"],
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elem_classes=["lex-table"],
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)
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out_neg = gr.Dataframe(
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label="Top 5 negative lex words (word, score)",
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headers=["word", "score"],
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elem_classes=["lex-table"],
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)
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load_btn.click(load_chats, inputs=[file_in], outputs=[chat_dd, state, status])
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