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app.py

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+import re
+import joblib
+import gradio as gr
+
+
+model = joblib.load("sentiment_model.pkl")
+
+
+def clean_text(text):
+    text = str(text).lower()
+    text = re.sub(r"http\S+|www\S+", "", text)
+    text = re.sub(r"@\w+", "", text)
+    text = re.sub(r"#", "", text)
+    text = re.sub(r"[^a-z0-9\s!?.,']", " ", text)
+    text = re.sub(r"\s+", " ", text).strip()
+    return text
+
+
+positive_words = {
+    "good", "great", "excellent", "useful", "helpful", "fast", "reliable",
+    "better", "smooth", "valuable", "enjoyable", "improves", "solved", "clear"
+}
+
+negative_words = {
+    "bad", "terrible", "poor", "slow", "weak", "worse", "crashing",
+    "disappointing", "confusing", "rude", "ignored", "harmful", "bugs", "wrong"
+}
+
+mixed_markers = {
+    "but", "however", "although", "though", "while"
+}
+
+
+def explain_sentiment(text):
+    cleaned = clean_text(text)
+    tokens = cleaned.split()
+
+    positive_clues = [word for word in tokens if word in positive_words]
+    negative_clues = [word for word in tokens if word in negative_words]
+    mixed_clues = [word for word in tokens if word in mixed_markers]
+
+    explanation = []
+
+    if positive_clues:
+        explanation.append(f"Positive clues found: {positive_clues}")
+
+    if negative_clues:
+        explanation.append(f"Negative clues found: {negative_clues}")
+
+    if mixed_clues:
+        explanation.append(f"Mixed-sentiment marker found: {mixed_clues}")
+
+    if not explanation:
+        explanation.append("No strong sentiment clue was found using the simple explanation layer.")
+
+    return explanation
+
+
+def analyze_sentiment(text):
+    cleaned = clean_text(text)
+
+    prediction = model.predict([cleaned])[0]
+
+    output = f"Predicted Sentiment: {prediction}\n\n"
+
+    if hasattr(model, "predict_proba"):
+        probabilities = model.predict_proba([cleaned])[0]
+        prob_table = sorted(
+            zip(model.classes_, probabilities),
+            key=lambda x: x[1],
+            reverse=True
+        )
+
+        confidence = max(probabilities)
+
+        output += f"Confidence: {confidence:.3f}\n\n"
+        output += "Probability Table:\n"
+
+        for label, prob in prob_table:
+            output += f"{label}: {prob:.3f}\n"
+
+        if confidence < 0.45:
+            output += "\nResearch Note: Low confidence. Human review may be needed.\n"
+        else:
+            output += "\nResearch Note: The model found a reasonably clear pattern.\n"
+
+    else:
+        output += "Confidence: This model does not provide probabilities.\n\n"
+
+    explanation = explain_sentiment(text)
+
+    output += "\nExplanation:\n"
+    for item in explanation:
+        output += f"- {item}\n"
+
+    return output
+
+
+demo = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(
+        lines=6,
+        placeholder="Paste a social media post, review, news sentence, or public comment here..."
+    ),
+    outputs=gr.Textbox(lines=18),
+    title="ToneLens AI — Sentiment Analyzer",
+    description="A student-built NLP product that analyzes sentiment in text using a trained machine learning model."
+)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+scikit-learn
+pandas
+numpy
+joblib

sentiment_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:973ae96c1a5bce9168ef21c9b328f683098bad974db7c3e3a3d8640cef904ebf
+size 209472