Update app.py

app.py

@@ -1,27 +1,32 @@
 import gradio as gr
 import joblib
 
-# Function to load your model (adjust the path and method if needed)
-def load_model():
-    # This path is relative to the root of your Hugging Face Space
-    model_path = "./en-hate-speech-detection-3label"
-    model = joblib.load(model_path)
-    return model
-
-# Function to predict hate speech from text input
-def predict_hate_speech(text):
-    model = load_model()  # Load your model
-    prediction = model.predict([text])
-    # Assuming your model outputs integers representing classes, you might want to convert
-    # these to more readable labels. Adjust these labels according to your model's output.
-    labels = {0: 'Neutral or Ambiguous', 1: 'Not Hate', 2: 'Offensive or Hate Speech'}
-    return labels[prediction[0]]
-
-# Adjusted Gradio interface to take text input and output model predictions
-iface = gr.Interface(fn=predict_hate_speech, 
-                     inputs=gr.Textbox(lines=2, placeholder="Enter Text Here..."), 
-                     outputs="text", 
-                     description="Detects hate speech in text. Outputs 'Neutral or Ambiguous', 'Not Hate', 'Offensive or Hate Speech'.")
+# Load your serialized objects
+model = joblib.load('random_forest_model_3labels2.joblib')
+encoder = joblib.load('label_encoder2.joblib')
+vectorizer = joblib.load('count_vectorizer2.joblib')
+
+def predict(input_text):
+    # Preprocess the input with your vectorizer and encoder as needed
+    # For example, if your model expects vectorized input:
+    vectorized_text = vectorizer.transform([input_text])
+    
+    # Make a prediction
+    prediction = model.predict(vectorized_text)
+    
+    # If your model's output needs to be decoded (optional)
+    # decoded_prediction = encoder.inverse_transform(prediction)
+    
+    # Return the prediction (you might want to convert it into a more readable form)
+    return prediction[0]  # Modify this according to your needs
+
+# Setup the Gradio interface
+iface = gr.Interface(fn=predict,
+                     inputs=gr.Textbox(lines=2, placeholder="Enter Text Here..."),
+                     outputs="text",
+                     description="Your model description here.")
+
+# Launch the app
 iface.launch()