app.py

import os
import gradio as gr
import json
from huggingface_hub import InferenceClient

# Load your Hugging Face token from environment variable
HF_TOKEN = os.getenv("HF_API_TOKEN")

# Initialize the inference client
client = InferenceClient(
    model="deepseek-ai/DeepSeek-V3",
    token=HF_TOKEN
)

# Load Quran words and language list
with open("words.json", encoding="utf-8") as f:
    word_list = json.load(f)

with open("language_list.json", encoding="utf-8") as f:
    language_list = json.load(f)

# Prepare dropdown options
word_options = [f"{word['text']} ({word['english']})" for word in word_list]
language_options = [f"{lang['name']} ({lang['code']})" for lang in language_list]


def create_messages(word_entry, language_name):
    return [
        {
            "role": "system",
            "content": "You are a helpful and friendly assistant that explains Quranic words in a simple way.",
        },
        {
            "role": "user",
            "content": f"""
Explain the Quranic word "{word_entry['text']}" (which means "{word_entry['english']}") in {language_name}.

Please include:
1. Translation in {language_name}
2. Root word and derivatives
3. Occurrences in the Qur'an (Surah & Verse)
4. Explanation of each occurrence using easy-to-understand {language_name}
""",
        },
    ]
    
# Keep a global/local cache (dict) to store responses
response_cache = {}

def process(word_label, lang_label):
    cache_key = (word_label, lang_label)

    # ✅ Return cached response directly if exists
    if cache_key in response_cache:
        yield response_cache[cache_key]
        return

    selected_word = next((w for w in word_list if w['text'] in word_label), None)
    language_name = lang_label.split("(")[1].strip() if "(" in lang_label else lang_label.strip()

    if not selected_word:
        yield "❌ Word not found."
        return

    messages = create_messages(selected_word, language_name)

    try:
        stream = client.chat.completions.create(
            messages=messages,
            temperature=0.7,
            top_p=0.9,
            max_tokens=1024,
            stream=True
        )

        output = ""
        for chunk in stream:
            if chunk.choices and chunk.choices[0].delta.content:
                output += chunk.choices[0].delta.content
                yield output

        # ✅ Store the final output in cache
        response_cache[cache_key] = output

    except Exception as e:
        yield f"❌ Error: {e}"


# Build Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 📖 Quran Word Explorer (with DeepSeek-V3) — Streaming Enabled")

    with gr.Row():
        word_input = gr.Dropdown(choices=word_options, label="🔤 Select Quranic Word")
        lang_input = gr.Dropdown(choices=language_options, label="🌐 Select Language")

    run_btn = gr.Button("🔍 Get Explanation")
    output = gr.Textbox(label="📘 Output", lines=20)

    run_btn.click(fn=process, inputs=[word_input, lang_input], outputs=output)

demo.launch()