Upload 4 files

app.py

@@ -0,0 +1,22 @@
+import gradio as gr
+import joblib
+
+# Load model & vectorizer
+model = joblib.load("model.joblib")
+tfidf = joblib.load("tfidf_vectorizer.joblib")
+
+def predict_sentiment(text):
+    vector = tfidf.transform([text])
+    prediction = model.predict(vector)[0]
+    label = "✅ Positive" if prediction == 1 else "❌ Negative"
+    return label
+
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(label="Enter a Review"),
+    outputs=gr.Textbox(label="Sentiment"),
+    title="XGBoost Sentiment Classifier",
+    description="Real-time sentiment analysis using TF-IDF + XGBoost."
+)
+
+iface.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+scikit-learn
+xgboost
+joblib
+pandas

sentiment_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd28e82d3707dcc3f543c3a35541dad91132f9e7ff9049fc55781adbfc018088
+size 40

trianed.py

@@ -0,0 +1,39 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from xgboost import XGBClassifier
+from sklearn.metrics import accuracy_score
+import joblib
+
+# Load dataset
+df = pd.read_csv("dummy_sentiment_dataset.csv")
+
+# Split
+X_train, X_test, y_train, y_test = train_test_split(
+    df["text"], df["label"], test_size=0.2, random_state=42
+)
+
+# TF-IDF
+tfidf = TfidfVectorizer(max_features=5000)
+X_train_tfidf = tfidf.fit_transform(X_train)
+X_test_tfidf = tfidf.transform(X_test)
+
+# Model
+model = XGBClassifier(
+    n_estimators=300,
+    max_depth=6,
+    learning_rate=0.1,
+    eval_metric='logloss'
+)
+
+model.fit(X_train_tfidf, y_train)
+
+# Evaluate
+y_pred = model.predict(X_test_tfidf)
+print("Accuracy:", accuracy_score(y_test, y_pred))
+
+# Save model + vectorizer
+joblib.dump(model, "model.joblib")
+joblib.dump(tfidf, "tfidf_vectorizer.joblib")
+
+print("✅ Model and vectorizer saved!")