app.py

import gradio as gr
import requests

# Define the API parameters
API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
API_TOKEN = os.getenv("HF_AUTH_TOKEN")
if not API_TOKEN:
    raise ValueError("Please set the HF_AUTH_TOKEN environment variable.")

headers = {"Authorization": f"Bearer {API_TOKEN}"}

# Function to query the API
def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# Function to be called by the Gradio interface
def evaluate_hallucination(input1, input2):
    # Combine the inputs
    combined_input = f"{input1}. {input2}"
    
    # Make the API call
    output = query({"inputs": combined_input})
    
    # Extract the score from the output
    score = output[0][0]['score']
    
    # Return a red or green circle based on the score
    if score < 0.5:
        return "🔴", "The score is less than 0.5"
    else:
        return "🟢", "The score is greater than 0.5"

# Create the Gradio interface
iface = gr.Interface(
    fn=evaluate_hallucination,
    inputs=[gr.inputs.Textbox(label="Input 1"), gr.inputs.Textbox(label="Input 2")],
    outputs=[gr.outputs.Label(), gr.outputs.Textbox(label="Explanation")],
    live=False
)

# Launch the interface
iface.launch()