Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the pre-trained model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
+
+# Suppress warning about weights not being initialized
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2, state_dict=model.state_dict() if not isinstance(model, type(model)) else None)
+
+# Define the prediction function
+def predict(text):
+    # If a single example is provided, convert it to a list
+    if isinstance(text, str):
+        text = [text]
+
+    # Encode the text into tokens
+    encoded_text = tokenizer(text, padding='max_length', truncation=True, max_length=512, return_tensors='pt')
+    input_ids = encoded_text['input_ids']
+    attention_mask = encoded_text['attention_mask']
+
+    # Run the text through the model
+    outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+    logits = outputs.logits
+
+    # Get the probability of hate speech
+    hate_speech_probability = torch.softmax(logits, dim=1)[:, 1].tolist()
+
+    # Determine the predictions
+    predictions = ["Hate speech" if prob > 0.5 else "Not hate speech" for prob in hate_speech_probability]
+
+    return predictions[0] if len(predictions) == 1 else predictions
+
+# Custom CSS styles
+custom_css = """
+<style>
+    .stTextInput {
+        width: 100%;
+        padding: 10px;
+        border: 1px solid #ddd;
+        border-radius: 5px;
+        margin-top: 10px;
+    }
+
+    .styled-button {
+        background-color: #4CAF50;
+        color: white;
+        padding: 10px 20px;
+        text-align: center;
+        text-decoration: none;
+        display: inline-block;
+        font-size: 16px;
+        cursor: pointer;
+        border-radius: 5px;
+        margin-top: 10px;
+    }
+
+    .styled-button:hover {
+        background-color: #45a049;
+    }
+
+    .stButton button {
+        background-color: #4CAF50;
+        color: white;
+        padding: 10px 20px;
+        text-align: center;
+        text-decoration: none;
+        display: inline-block;
+        font-size: 16px;
+        cursor: pointer;
+        border-radius: 5px;
+    }
+
+    .stButton button:hover {
+        background-color: #45a049;
+    }
+
+    .stRadio {
+        padding: 10px;
+        border: 1px solid #ddd;
+        border-radius: 5px;
+        margin-top: 10px;
+    }
+</style>
+"""
+
+# Inject custom CSS
+st.markdown(custom_css, unsafe_allow_html=True)
+
+# Create the Streamlit app with a navigation bar
+st.title("Hate Speech Detector")
+
+# Sidebar for navigation
+nav_option = st.sidebar.radio("Navigation", ["Text Input", "CSV Upload"])
+
+# Check the chosen navigation option
+if nav_option == "Text Input":
+    # Option to input text directly
+    text_input = st.text_area("Enter your text here:")
+    
+    if st.button("Predict"):
+        # If text is entered, use that for prediction
+        if text_input:
+            prediction = predict(text_input)
+            st.subheader("Prediction:")
+            st.write(prediction)
+        else:
+            st.warning("Please enter text before clicking 'Predict'.")
+
+elif nav_option == "CSV Upload":
+    # Option to upload a CSV file
+    uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+    
+    if st.button("Predict"):
+        # If a CSV file is uploaded, use the first column for prediction
+        if uploaded_file:
+            df = pd.read_csv(uploaded_file)
+            if not df.empty and not df.columns.empty:
+                text_column = df.columns[0]
+                predictions = df[text_column].apply(predict)
+                st.subheader("Predictions:")
+                st.write(predictions)
+            else:
+                st.warning("The CSV file is empty or does not have a valid column.")
+        else:
+            st.warning("Please upload a CSV file before clicking 'Predict'.")