Create app.py

app.py

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+
+import re, os, tempfile, math
+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+from functools import lru_cache
+from transformers import pipeline
+import emoji
+from langdetect import detect, DetectorFactory
+DetectorFactory.seed = 0  # deterministic language detection
+
+# ----------------------
+# Models and pipelines
+# ----------------------
+SENTIMENT_CHOICES = {
+    "SST-2 (binary, reviews)": "distilbert-base-uncased-finetuned-sst-2-english",  # POSITIVE/NEGATIVE
+    "Twitter (EN, 3-class)": "cardiffnlp/twitter-roberta-base-sentiment-latest",    # NEGATIVE/NEUTRAL/POSITIVE
+    "Twitter (Multilingual, 3-class)": "cardiffnlp/twitter-xlm-roberta-base-sentiment"
+}
+EMOTION_MODEL = "bhadresh-savani/distilbert-base-uncased-emotion"  # sadness/joy/love/anger/fear/surprise
+
+@lru_cache(maxsize=8)
+def get_pipe(task, model_id):
+    # device_map="auto" if GPU is present in Colab; else CPU fallback
+    return pipeline(task, model=model_id, tokenizer=model_id if "cardiffnlp" in model_id else None)
+
+# ----------------------
+# Text utilities
+# ----------------------
+URL_RE = re.compile(r"https?://\S+|www\.\S+")
+MENTION_RE = re.compile(r"@\w+")
+HASHTAG_RE = re.compile(r"#(\w+)")
+
+def clean_line(s, demojize=True, strip_social=True, lower=False):
+    if demojize:
+        s = emoji.demojize(s, language='en')
+    if strip_social:
+        s = URL_RE.sub("", s)
+        s = MENTION_RE.sub("", s)
+        # Keep hashtag token but turn into plain word
+        s = HASHTAG_RE.sub(r"\1", s)
+    s = s.strip()
+    if lower:
+        s = s.lower()
+    return s
+
+def detect_langs(lines, probe=30):
+    # quick language probe on a sample
+    sample = [l for l in lines if l.strip()][:probe]
+    counts = {}
+    for s in sample:
+        try:
+            code = detect(s)
+            counts[code] = counts.get(code, 0) + 1
+        except:
+            counts["unk"] = counts.get("unk", 0) + 1
+    total = sum(counts.values()) or 1
+    share_en = counts.get("en", 0) / total
+    return counts, share_en
+
+# ----------------------
+# Core analyzer
+# ----------------------
+def run_analysis(
+    text_block, file_obj, text_col, mode, sentiment_model_choice, auto_model, demojize_opt,
+    strip_social_opt, lower_opt, batch_size
+):
+    # Collect lines from textbox and/or CSV
+    lines = []
+    if text_block:
+        lines.extend([l.rstrip() for l in text_block.splitlines() if l.strip()])
+
+    df_in = None
+    if file_obj is not None:
+        try:
+            df_in = pd.read_csv(file_obj.name)
+            use_col = text_col if (text_col and text_col in df_in.columns) else None
+            if not use_col:
+                # naive auto-pick
+                for c in ["text", "message", "msg", "content", "body"]:
+                    if c in df_in.columns:
+                        use_col = c
+                        break
+            if not use_col:
+                return (pd.DataFrame([{"error": "CSV loaded, but no text column selected/found."}]),
+                        plt.figure(), gr.update(value=None), "No language info")
+            lines.extend([str(x) for x in df_in[use_col].astype(str).tolist()])
+        except Exception as e:
+            return (pd.DataFrame([{"error": f"Failed to read CSV: {e}"}]),
+                    plt.figure(), gr.update(value=None), "No language info")
+
+    if not lines:
+        return (pd.DataFrame([{"error": "Enter text or upload CSV with a text column."}]),
+                plt.figure(), gr.update(value=None), "No language info")
+
+    # Preprocess
+    proc = [clean_line(l, demojize=demojize_opt, strip_social=strip_social_opt, lower=lower_opt) for l in lines]
+
+    # Language probe to optionally switch sentiment model
+    lang_counts, share_en = detect_langs(proc, probe=min(30, len(proc)))
+    lang_info = f"Lang probe (top): {dict(sorted(lang_counts.items(), key=lambda x: -x[1])[:3])}, EN share≈{round(share_en,2)}"
+
+    # Choose model
+    if mode == "Sentiment":
+        if auto_model:
+            model_id = SENTIMENT_CHOICES["Twitter (EN, 3-class)"] if share_en >= 0.6 else SENTIMENT_CHOICES["Twitter (Multilingual, 3-class)"]
+        else:
+            model_id = SENTIMENT_CHOICES[sentiment_model_choice]
+        pipe = get_pipe("sentiment-analysis", model_id)
+    else:
+        pipe = get_pipe("text-classification", EMOTION_MODEL)
+
+    # Batched inference for speed
+    outputs = []
+    for i in range(0, len(proc), batch_size):
+        batch = proc[i:i+batch_size]
+        outs = pipe(batch, batch_size=batch_size, truncation=True)
+        # normalize to list[dict]
+        for out in outs:
+            out0 = out[0] if isinstance(out, list) else out
+            outputs.append({"label": out0["label"], "score": float(out0["score"])})
+
+    # Build results DataFrame
+    rows = []
+    for idx, (raw, out) in enumerate(zip(lines, outputs), 1):
+        rows.append({
+            "idx": idx,
+            "text": raw,
+            "label": out["label"],
+            "score": round(out["score"], 4)
+        })
+    df = pd.DataFrame(rows)
+
+    # Distribution plot (matplotlib)
+    counts = df["label"].value_counts().sort_values(ascending=False)
+    fig, ax = plt.subplots(figsize=(6.5, 3.2))
+    counts.plot(kind="bar", ax=ax, color="#4C78A8")
+    ax.set_title("Label Distribution")
+    ax.set_xlabel("Label")
+    ax.set_ylabel("Count")
+    plt.tight_layout()
+
+    # Export to CSV
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+    df.to_csv(tmp.name, index=False)
+
+    return df, fig, tmp.name, lang_info
+
+# ----------------------
+# UI
+# ----------------------
+with gr.Blocks(title="Chat Mood Analyzer — Ultimate") as demo:
+    gr.Markdown("## Chat Mood Analyzer — Ultimate Edition")
+
+    with gr.Row():
+        with gr.Column():
+            txt = gr.Textbox(lines=10, label="Paste chat (one message per line)")
+            file_in = gr.File(label="Or upload CSV", file_types=[".csv"], file_count="single")
+            text_col = gr.Textbox(value="", label="CSV text column (auto-detect if blank)")
+
+            mode = gr.Radio(["Sentiment", "Emotion"], value="Sentiment", label="Analysis mode")
+
+            with gr.Accordion("Sentiment model settings", open=False):
+                auto_model = gr.Checkbox(value=True, label="Auto-pick tweet-aware EN vs Multilingual")
+                sentiment_model = gr.Dropdown(
+                    choices=list(SENTIMENT_CHOICES.keys()),
+                    value="Twitter (EN, 3-class)",
+                    label="Manual model (used if Auto is OFF)"
+                )
+                gr.Markdown("Tip: Twitter models understand slang/emojis better than SST‑2 review models.")
+
+            with gr.Accordion("Preprocessing", open=False):
+                demojize_opt = gr.Checkbox(value=True, label="Convert emojis to text (:face_with_tears_of_joy:)")
+                strip_social_opt = gr.Checkbox(value=True, label="Strip URLs/@mentions/#hashtags")
+                lower_opt = gr.Checkbox(value=False, label="Lowercase text")
+
+            batch_size = gr.Slider(1, 64, value=16, step=1, label="Batch size")
+
+            run = gr.Button("Analyze", variant="primary")
+            clear = gr.ClearButton([txt, file_in])
+
+        with gr.Column():
+            out_table = gr.Dataframe(label="Per-message results", wrap=True)
+            out_plot = gr.Plot(label="Label distribution")
+            download = gr.File(label="Download results (.csv)")
+            lang_probe = gr.Markdown()
+
+    evt = run.click(
+        fn=run_analysis,
+        inputs=[txt, file_in, text_col, mode, sentiment_model, auto_model,
+                demojize_opt, strip_social_opt, lower_opt, batch_size],
+        outputs=[out_table, out_plot, download, lang_probe],
+        concurrency_limit=4,
+        show_progress=True
+    )
+
+    demo.queue(max_size=64, default_concurrency_limit=2)
+    demo.launch(share=True)