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# Check for PyTorch installation
try:
import torch
print(f"PyTorch version: {torch.__version__}")
except ImportError:
print("PyTorch is not installed. Please install PyTorch to run this script.")
raise
from transformers import pipeline
import gradio as gr
# Initialize models as None
model1 = None
model2 = None
# Attempt to load the models and run test predictions
try:
model1_name = "JimminDev/jim-text-class"
model2_name = "JimminDev/Depressive-detector"
print("Loading models...")
model1 = pipeline("text-classification", model=model1_name)
test_output1 = model1("Testing the first model with a simple sentence.")
print("Harassment Detector test output:", test_output1)
model2 = pipeline("text-classification", model=model2_name)
test_output2 = model2("Testing the second model with a simple sentence.")
print("Depressive Detector test output:", test_output2)
except Exception as e:
print(f"Failed to load or run models: {e}")
# Prediction function with model selection and error handling
def predict_sentiment(text, model_choice):
try:
if model_choice == "Harassment Detector":
if model1 is None:
raise ValueError("Harassment Detector not loaded.")
predictions = model1(text)
elif model_choice == "Depressive Detector":
if model2 is None:
raise ValueError("Depressive Detector not loaded.")
predictions = model2(text)
else:
raise ValueError("Invalid model choice.")
return f"Label: {predictions[0]['label']}, Score: {predictions[0]['score']:.4f}"
except Exception as e:
return f"Error processing input: {e}"
# Define example inputs
examples = [
["I absolutely love this product! It has changed my life.", "Harassment Detector"],
["This is the worst movie I have ever seen. Completely disappointing.", "Harassment Detector"],
["I'm not sure how I feel about this new update. It has some good points, but also many drawbacks.", "Depressive Detector"],
["The customer service was fantastic! Very helpful and polite.", "Depressive Detector"],
["Honestly, this was quite a mediocre experience. Nothing special.", "Harassment Detector"]
]
# Gradio interface setup
iface = gr.Interface(
fn=predict_sentiment,
title="Sentiment Analysis",
description="Enter text to analyze sentiment. Powered by Hugging Face Transformers.",
inputs=[
gr.Textbox(lines=2, placeholder="Enter text here..."),
gr.Radio(choices=["Harassment Detector", "Depressive Detector"], label="Select Model")
],
outputs="text",
examples=examples
)
if __name__ == "__main__":
iface.launch()
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