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import os |
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import re |
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import joblib |
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import numpy as np |
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import gradio as gr |
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def try_load(path_options): |
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for p in path_options: |
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if p is None: |
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continue |
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if os.path.exists(p): |
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try: |
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model = joblib.load(p) |
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print(f"Loaded: {p}") |
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return model, p |
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except Exception as e: |
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print(f"Failed to load {p}: {e}") |
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return None, None |
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ROOT = os.path.dirname(__file__) if "__file__" in globals() else os.getcwd() |
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MODEL_DIR = os.path.join(ROOT, "models") |
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tfidf_candidates = [ |
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os.path.join(MODEL_DIR, "tfidf_vectorizer.pkl"), |
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os.path.join(MODEL_DIR, "tfidf.pkl"), |
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os.path.join(ROOT, "tfidf_vectorizer.pkl"), |
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os.path.join(ROOT, "tfidf.joblib"), |
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os.path.join(MODEL_DIR, "tfidf_vectorizer.joblib"), |
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os.path.join(MODEL_DIR, "tfidf.joblib"), |
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] |
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logreg_candidates = [ |
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os.path.join(MODEL_DIR, "logreg_model.pkl"), |
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os.path.join(MODEL_DIR, "logreg.pkl"), |
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os.path.join(ROOT, "logreg_model.pkl"), |
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os.path.join(ROOT, "logreg.pkl"), |
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os.path.join(MODEL_DIR, "logreg.joblib"), |
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] |
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lgbm_candidates = [ |
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os.path.join(MODEL_DIR, "lgbm_model.pkl"), |
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os.path.join(MODEL_DIR, "lgbm.pkl"), |
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os.path.join(ROOT, "lgbm_model.pkl"), |
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os.path.join(ROOT, "lgbm.pkl"), |
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] |
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tfidf, tfidf_path = try_load(tfidf_candidates) |
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logreg, logreg_path = try_load(logreg_candidates) |
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lgbm, lgbm_path = try_load(lgbm_candidates) |
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DEFAULT_LABELS = ['negative', 'neutral', 'positive'] |
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def clean_text(t): |
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if t is None: |
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return "" |
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s = str(t).lower() |
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s = re.sub(r"\s+", " ", s) |
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s = re.sub(r"[^a-z0-9\s']", " ", s) |
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return s.strip() |
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import warnings |
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warnings.filterwarnings("ignore") |
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def get_model_classes(model): |
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if hasattr(model, "classes_"): |
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return list(model.classes_) |
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if hasattr(model, "classes"): |
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return list(model.classes) |
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return DEFAULT_LABELS |
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def predict_one(text, model_choice="Logistic Regression"): |
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text_clean = clean_text(text) |
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if not text_clean: |
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return { |
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"label": "neutral", |
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"confidence": 0.0, |
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"html": "<i>No text provided</i>", |
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"error": None |
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} |
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if tfidf is None: |
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return {"label": None, "confidence": 0.0, "html": "", "error": "Vectorizer (tfidf) not found. Upload tfidf_vectorizer.pkl to models/."} |
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X = tfidf.transform([text_clean]) |
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try: |
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if model_choice == "Logistic Regression" and logreg is not None: |
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probs = logreg.predict_proba(X)[0] |
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classes = get_model_classes(logreg) |
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elif model_choice == "LightGBM" and lgbm is not None: |
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try: |
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probs = lgbm.predict_proba(X)[0] |
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except Exception: |
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probs = lgbm.predict_proba(X.toarray())[0] |
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classes = get_model_classes(lgbm) |
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else: |
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if logreg is not None: |
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probs = logreg.predict_proba(X)[0]; classes = get_model_classes(logreg) |
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elif lgbm is not None: |
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try: probs = lgbm.predict_proba(X)[0] |
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except: probs = lgbm.predict_proba(X.toarray())[0] |
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classes = get_model_classes(lgbm) |
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else: |
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return {"label": None, "confidence": 0.0, "html": "", "error": "No model found. Upload logreg_model.pkl or lgbm_model.pkl to models/."} |
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except Exception as e: |
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return {"label": None, "confidence": 0.0, "html": "", "error": f"Prediction error: {e}"} |
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idx = int(np.argmax(probs)) |
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label = classes[idx] |
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confidence = float(probs[idx]) |
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colors = { |
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'positive': '#16a34a', |
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'neutral': '#f59e0b', |
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'negative': '#ef4444' |
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} |
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bars_html = "" |
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for c, p in zip(classes, probs): |
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col = colors.get(str(c).lower(), "#3b82f6") |
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pct = float(p) * 100.0 |
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bars_html += f""" |
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<div style="display:flex;align-items:center;margin-bottom:8px;"> |
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<div style="width:95px;font-weight:600;color:#e5e7eb;">{c}</div> |
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<div style="flex:1;margin-left:10px;background:#1f2937;border-radius:999px;padding:3px;"> |
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<div style="width:{pct:.2f}%;background:{col};padding:6px 10px;border-radius:999px;color:white;font-weight:700;text-align:right;"> |
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{pct:.1f}% |
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</div> |
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</div> |
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</div> |
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""" |
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header_html = f""" |
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<div style="display:flex;align-items:center;gap:12px;"> |
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<div style="font-size:16px;font-weight:700;color:#f3f4f6;">Prediction:</div> |
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<div style="padding:6px 12px;border-radius:999px;background:{colors.get(str(label).lower(),'#3b82f6')};color:white;font-weight:800;"> |
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{label.upper()} ({confidence:.2f}) |
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</div> |
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</div> |
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<div style="margin-top:12px;">{bars_html}</div> |
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""" |
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return {"label": label, "confidence": float(confidence), "html": header_html, "error": None} |
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css = """ |
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body { background: linear-gradient(135deg,#0f172a 0%,#1e293b 100%); color:#e5e7eb; } |
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.app-card { |
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border-radius: 12px; |
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padding: 18px; |
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box-shadow: 0 10px 25px rgba(0, 0, 0, 0.6); |
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background: rgba(31,41,55,0.9); |
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color: #f9fafb; |
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} |
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.title { |
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font-weight: 800; |
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font-size: 22px; |
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margin-bottom: 6px; |
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color: #f9fafb; |
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} |
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.subtitle { |
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color: #9ca3af; |
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margin-bottom: 12px; |
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} |
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.gr-button { |
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border-radius: 10px; |
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padding: 10px 16px; |
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font-weight:700; |
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background: #3b82f6 !important; |
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color: white !important; |
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} |
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""" |
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examples = [ |
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["Looking forward to our demo next week! Confirm time please.", "Logistic Regression"], |
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["Not interested at this time, thanks.", "LightGBM"], |
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["Can you share pricing and features?", "Logistic Regression"], |
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] |
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with gr.Blocks(css=css, theme=gr.themes.Base()) as demo: |
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with gr.Row(): |
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with gr.Column(scale=2): |
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gr.HTML("<div class='app-card'><div class='title'>🌙 SvaraAI — Reply Classifier</div>" |
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"<div class='subtitle'>Classify replies as <b style='color:#16a34a'>positive</b> / <b style='color:#f59e0b'>neutral</b> / <b style='color:#ef4444'>negative</b>.</div>" |
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"</div>") |
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inp = gr.Textbox(lines=5, placeholder="Type your reply here...", label="Reply text") |
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model_choice = gr.Dropdown(choices=["Logistic Regression", "LightGBM"], value="Logistic Regression", label="Model") |
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with gr.Row(): |
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btn = gr.Button("🚀 Classify", variant="primary") |
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clear = gr.Button("🧹 Clear") |
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output_label = gr.Markdown(value="**Prediction:** _waiting for input_", label="Result") |
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output_html = gr.HTML("<i style='color:#9ca3af;'>Probabilities will appear here</i>") |
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error_box = gr.Textbox(interactive=False, visible=False) |
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gr.Examples(examples=examples, inputs=[inp, model_choice], label="Try these examples") |
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with gr.Column(scale=1): |
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gr.HTML("<div class='app-card'><div style='font-weight:800;margin-bottom:8px'>ℹ️ About</div>" |
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"<div style='font-size:13px;color:#d1d5db'>This demo uses a TF-IDF vectorizer and a saved classifier (Logistic Regression / LightGBM). " |
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"Upload your saved pickles to <code>models/</code> as described in README.md.</div></div>") |
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stats_md = gr.Markdown("**Model files detected:**<br>" |
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f"- TF-IDF: `{tfidf_path or 'NOT FOUND'}` \n" |
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f"- LogReg: `{logreg_path or 'NOT FOUND'}` \n" |
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f"- LGBM: `{lgbm_path or 'NOT FOUND'}` \n") |
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download_note = gr.Markdown("<small style='color:#9ca3af;'>If a model is missing upload it to <code>models/</code> or rename files appropriately.</small>") |
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def run_and_format(text, model_choice): |
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res = predict_one(text, model_choice) |
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if res.get("error"): |
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return f"**Error:** {res['error']}", "", gr.update(value=f"<div style='color:#ef4444;font-weight:700'>{res['error']}</div>") |
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label = res["label"] |
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conf = res["confidence"] |
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html = res["html"] |
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md = f"**Prediction:** **{label.upper()}** — confidence **{conf:.2f}**" |
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return md, str(round(conf, 3)), gr.update(value=html) |
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btn.click(run_and_format, inputs=[inp, model_choice], outputs=[output_label, error_box, output_html]) |
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clear.click(lambda: ("**Prediction:** _waiting for input_", "", gr.update(value="<i style='color:#9ca3af;'>Probabilities will appear here</i>")), [], [output_label, error_box, output_html]) |
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gr.HTML("<div style='margin-top:18px;color:#9ca3af;font-size:13px'>🌌 Built for the SvaraAI assignment • Upload your model pickles into <code>models/</code></div>") |
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if __name__ == "__main__": |
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demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860))) |
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