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)