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import gradio as gr
import requests
# Define the sentiment analysis function that communicates with the Hugging Face model API
def analyze_sentiment(message: str):
try:
# Send request to the Hugging Face Space for sentiment analysis
response = requests.post(
'https://safwansajad-emotion-detection-gpt.hf.space/predict',
json={'text': message},
headers={'Content-Type': 'application/json'}
)
# Extract the sentiment and score from the response
data = response.json()
# Return the label and score in the format expected by your app
if 'emotion' in data and 'score' in data:
return [{"label": data['emotion'], "score": data['score']}]
else:
return [{"label": "Unknown", "score": 0}]
except Exception as e:
print(f"Error during sentiment analysis: {e}")
return [{"label": "Error", "score": 0}]
# Set up Gradio interface
iface = gr.Interface(
fn=analyze_sentiment, # Function to be called
inputs=gr.Textbox(label="Enter your message", placeholder="How are you feeling today?", lines=2), # User input
outputs=gr.JSON(), # Output in JSON format (label and score)
live=True, # Enables live input processing
title="Sentiment Analysis with SerenityAI", # Title of the interface
description="Enter a message and get feedback about your emotional state. Your feelings matter!",
theme="huggingface", # Optionally set the theme to Hugging Face's style
)
# Launch the Gradio app
if __name__ == "__main__":
iface.launch(share=True) # share=True gives you a public link |