Create app.py

app.py

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+import torch
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+def load_model(model_path, device):
+    model = DistilBertForSequenceClassification.from_pretrained(model_path)
+    model.to(device)
+    model.eval()
+    return model
+
+def run_inference(model, tokenizer, label_decoder, device, user_input):
+    model.eval()  # Set the model to evaluation mode
+
+    # user_input = input("Enter a text for prediction: ")
+
+    # Tokenize user input
+    input_ids = tokenizer.encode(user_input, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        outputs = model(input_ids)
+        predicted_label = torch.argmax(outputs.logits, dim=1).tolist()
+
+        # Extracting the text and predicted outcome
+        input_text = tokenizer.decode(input_ids[0], skip_special_tokens=True)
+        predicted_outcome = label_decoder[predicted_label[0]]
+
+        # Display the results
+        print(f"Text: {input_text}")
+        print(f"Predicted Outcome: {predicted_outcome}")
+        print()
+        return predicted_outcome  # Add a new line for better readability
+
+# Example usage
+model_path = "/home/lwasinam/AI_Projects/hate_speech_detection/model6"  # Replace with the actual path to your model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")  # Replace with your desired tokenizer
+
+# Load model
+model = load_model(model_path, device)
+
+
+label_decoder = {0: "Not Hate", 1: "Hate",}
+
+
+# Assuming you have label_decoder defined
+
+
+import streamlit as st
+
+st.title("Hate Speech Detection")
+
+user_input = st.text_input("Enter your text:")
+if user_input:
+    result = run_inference(model, tokenizer, label_decoder, device, user_input)
+    st.write("Inference Result:", result)