import gradio as gr
import requests
import numpy as np
import os

# إعدادات الموديل
API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/paraphrase-mpnet-base-v2"
HEADERS = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_TOKEN')}"}

def get_embedding(text):
    response = requests.post(API_URL, headers=HEADERS, json={"inputs": text})
    if response.status_code == 200:
        return np.array(response.json()[0])
    else:
        return None

def cosine_similarity(vec1, vec2):
    if vec1 is None or vec2 is None:
        return "❌ Error fetching embeddings"
    dot = np.dot(vec1, vec2)
    norm_a = np.linalg.norm(vec1)
    norm_b = np.linalg.norm(vec2)
    if norm_a == 0 or norm_b == 0:
        return 0.0
    return float(dot / (norm_a * norm_b))

def compare_sentences(sentence1, sentence2):
    embedding1 = get_embedding(sentence1)
    embedding2 = get_embedding(sentence2)
    similarity = cosine_similarity(embedding1, embedding2)
    return {
        "Sentence 1": sentence1,
        "Sentence 2": sentence2,
        "Cosine Similarity": round(similarity, 4) if isinstance(similarity, float) else similarity
    }

# واجهة Gradio
iface = gr.Interface(
    fn=compare_sentences,
    inputs=[
        gr.Textbox(label="Sentence 1", placeholder="Enter the first sentence..."),
        gr.Textbox(label="Sentence 2", placeholder="Enter the second sentence...")
    ],
    outputs="json",
    title="Sentence Similarity using HuggingFace API",
    description="Compares two sentences using the `paraphrase-mpnet-base-v2` model and returns the cosine similarity."
)

iface.launch(share=True)