Upload 4 files

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+
+# Load model and vectorizer
+model = joblib.load("logistic_model.pkl")
+vectorizer = joblib.load("tfidf_vectorizer.pkl")
+
+# Prediction function
+def predict_disaster(text):
+    text_df = pd.DataFrame([text], columns=["text"])
+    text_transformed = vectorizer.transform(text_df["text"])
+    prediction = model.predict(text_transformed)
+    label = "Disaster Tweet" if prediction[0] == 1 else "Not a Disaster Tweet"
+    return label
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_disaster,
+    inputs=gr.Textbox(lines=4, placeholder="Enter the tweet here..."),
+    outputs="text",
+    title="Disaster Tweet Classifier",
+    description="Classifies whether a tweet is about a real disaster or not.",
+)
+
+iface.launch()

logistic_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff47ef384e6c40e56a53056ecb03993ba5970b9df7e4a7a2016904950ff9462a
+size 4778322

requirements.txt

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+gradio
+scikit-learn
+joblib
+pandas

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eaf14e82d92a2b2aae5f6f2be9bb8cff2fc0f37ab3d511014bfa9db2c2539171
+size 178757