Update app.py

app.py

@@ -1,10 +1,16 @@
-import gradio as gr
-import requests
 import os
-import requests
+import gradio as gr
 import json
+from huggingface_hub import InferenceClient
+
+# Load environment token
+HF_TOKEN = os.getenv("HF_API_TOKEN")
+client = InferenceClient(
+    model="deepseek-ai/DeepSeek-V3",
+    token=HF_TOKEN
+)
 
-# Gradio-compatible data loading (as before)
+# Load word and language data
 with open("top_500_quran_lemmas_fixed.json", encoding="utf-8") as f:
     word_list = json.load(f)
 
@@ -14,55 +20,60 @@ with open("language_list.json", encoding="utf-8") as f:
 word_options = [f"{word['text']} ({word['english']})" for word in word_list]
 language_options = [f"{lang['name']} ({lang['code']})" for lang in language_list]
 
-# Use Hugging Face Inference API
-API_URL = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-V3"
-HEADERS = {"Authorization": f"Bearer {os.getenv('HF_API_TOKEN')}"}
-
-def query_hf_model(payload):
-    response = requests.post(API_URL, headers=HEADERS, json=payload)
-    return response.json()
-
-def create_prompt(word_entry, language_code):
-    return f"""
-You are a friendly Quranic AI assistant.
 
-The word is: {word_entry['text']} ({word_entry['english']}).
+def create_messages(word_entry, language_code):
+    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_code}.
 
-Please provide the following in easy-to-understand {language_code}:
-1. Translation of the word.
-2. Its root word and any related derivatives.
-3. Where it appears in the Qur'an (surah and ayah).
-4. Simple explanation for each occurrence.
+Please include:
+1. Translation in {language_code}
+2. Root word and derivatives
+3. Occurrences in the Qur'an (Surah & Verse)
+4. Explanation of each occurrence using easy language
+""",
+        },
+    ]
 
-Avoid technical terms. Think like a teacher explaining clearly to students.
-""".strip()
 
 def process(word_label, lang_label):
     selected_word = next((w for w in word_list if w['text'] in word_label), None)
     language_code = lang_label.split("(")[-1].strip(")")
+
     if not selected_word:
-        return "Word not found."
+        return "❌ Word not found."
+
+    messages = create_messages(selected_word, language_code)
+
+    try:
+        response = client.chat.completions.create(
+            messages=messages,
+            temperature=0.7,
+            top_p=0.9,
+            max_tokens=1024,
+            stream=False
+        )
+        return response.choices[0].message.content
 
-    prompt = create_prompt(selected_word, language_code)
-    output = query_hf_model({"inputs": prompt})
+    except Exception as e:
+        return f"❌ Error: {e}"
 
-    # Some formatting safety
-    if isinstance(output, dict) and "error" in output:
-        return f"Error: {output['error']}"
-    elif isinstance(output, list):
-        return output[0].get("generated_text", "No response.")
-    else:
-        return str(output)
 
 # Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 📖 Quran Word Explorer using DeepSeek-V3 (HF Inference API)")
+    gr.Markdown("## Quran Word Info (Powered by DeepSeek-V3)")
     with gr.Row():
-        word_input = gr.Dropdown(choices=word_options, label="Select a Quran Word")
+        word_input = gr.Dropdown(choices=word_options, label="Select Word")
         lang_input = gr.Dropdown(choices=language_options, label="Select Language")
-
-    output = gr.Textbox(label="DeepSeek-V3 Output", lines=20)
     run_btn = gr.Button("Get Info")
+    output = gr.Textbox(lines=20, label="Response")
+
     run_btn.click(fn=process, inputs=[word_input, lang_input], outputs=output)
 
-demo.launch()
+demo.launch()