Update app.py

app.py

@@ -1,22 +1,22 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 import torch
 import spaces
 import datetime
 import json
 import os
 from datasets import Dataset
-from huggingface_hub import HfFolder
+from huggingface_hub import HfApi
 
-# ✅ Authenticate Hugging Face account (save token only once)
-HfFolder.save_token("hf_your_token_here")  # ← Replace with your real HF token
+# ✅ Get token securely from environment
+token = os.environ.get("HF_TOKEN")
 
-# Define dropdown options
+# Dropdowns
 grade_options = ["1", "2", "3", "4", "5", "6"]
 topic_options = ["Addition", "Subtraction", "Counting", "Number Recognition", "Multiplication", "Division"]
 level_options = ["Beginner", "Intermediate", "Advanced"]
 
-# ✅ Save lesson to Hugging Face Datasets
+# ✅ Save generated lesson to Hugging Face Dataset
 def save_to_hf_dataset(prompt, output, repo_id="Pisethan/khmer-lesson-history"):
     timestamp = datetime.datetime.now().isoformat()
     record = {
@@ -25,25 +25,24 @@ def save_to_hf_dataset(prompt, output, repo_id="Pisethan/khmer-lesson-history"):
         "lesson": output
     }
 
-    # Save locally
     os.makedirs("history", exist_ok=True)
-    local_path = f"history/lesson_{timestamp.replace(':', '-')}.json"
-    with open(local_path, "w", encoding="utf-8") as f:
+    path = f"history/lesson_{timestamp.replace(':', '-')}.json"
+    with open(path, "w", encoding="utf-8") as f:
         json.dump(record, f, ensure_ascii=False, indent=2)
 
-    # Push to HF Dataset
     dataset = Dataset.from_list([record])
-    dataset.push_to_hub(repo_id)
+    dataset.push_to_hub(repo_id, token=token)
 
 @spaces.GPU
 def generate_lesson(grade, topic, level):
     device = 0 if torch.cuda.is_available() else -1
-    tokenizer = AutoTokenizer.from_pretrained("Pisethan/khmer-lesson-model")
+    tokenizer = AutoTokenizer.from_pretrained("Pisethan/khmer-lesson-model", use_auth_token=token)
     pipe = pipeline(
         "text-generation",
         model="Pisethan/khmer-lesson-model-v2",
         tokenizer=tokenizer,
-        device=device
+        device=device,
+        use_auth_token=token
     )
 
     prompt = f"""
@@ -61,28 +60,22 @@ Grade: {grade}
 Topic: {topic}
 TaRL Level: {level}
 """
-    output = pipe(
-        prompt,
-        max_new_tokens=300,
-        temperature=0.7,
-        do_sample=True,
-        eos_token_id=tokenizer.eos_token_id
-    )
 
-    # ✅ Save lesson
-    save_to_hf_dataset(prompt, output[0]["generated_text"])
+    output = pipe(prompt, max_new_tokens=300, temperature=0.7, do_sample=True, eos_token_id=tokenizer.eos_token_id)
 
-    return output[0]['generated_text']
+    save_to_hf_dataset(prompt, output[0]["generated_text"])
+    return output[0]["generated_text"]
 
 @spaces.GPU
 def generate_all_lessons():
     device = 0 if torch.cuda.is_available() else -1
-    tokenizer = AutoTokenizer.from_pretrained("Pisethan/khmer-lesson-model")
+    tokenizer = AutoTokenizer.from_pretrained("Pisethan/khmer-lesson-model", use_auth_token=token)
     pipe = pipeline(
         "text-generation",
         model="Pisethan/khmer-lesson-model-v2",
         tokenizer=tokenizer,
-        device=device
+        device=device,
+        use_auth_token=token
     )
 
     results = ""
@@ -104,26 +97,22 @@ Grade: {grade}
 Topic: {topic}
 TaRL Level: {level}
 """
-                output = pipe(
-                    prompt,
-                    max_new_tokens=300,
-                    temperature=0.7,
-                    do_sample=True,
-                    eos_token_id=tokenizer.eos_token_id
-                )
-                results += f"🔹 ថ្នាក់ {grade} | {topic} | {level}\n{output[0]['generated_text']}\n\n{'-'*50}\n\n"
-                save_to_hf_dataset(prompt, output[0]["generated_text"])  # ✅ Save each one
+                output = pipe(prompt, max_new_tokens=300, temperature=0.7, do_sample=True, eos_token_id=tokenizer.eos_token_id)
+                lesson = output[0]["generated_text"]
+                results += f"🔹 ថ្នាក់ {grade} | {topic} | {level}\n{lesson}\n\n{'-'*50}\n\n"
+                save_to_hf_dataset(prompt, lesson)
+
     return results
 
-# Gradio Interface
+# UI
 with gr.Blocks() as demo:
     gr.Markdown("## 🤖 អ្នកជំនួយបង្កើតមេរៀនគណិតវិទ្យា")
     gr.Markdown("ជ្រើសរើសថ្នាក់ ប្រធានបទ និងកម្រិតសិស្ស រួចចុចបង្កើតមេរៀន។ ឬចុចប៊ូតុងខាងក្រោមសម្រាប់បង្កើតមេរៀនទាំងអស់។")
 
     with gr.Row():
-        grade = gr.Dropdown(choices=grade_options, label="ថ្នាក់ (Grade)", value="1")
-        topic = gr.Dropdown(choices=topic_options, label="ប្រធានបទ (Topic)", value="Addition")
-        level = gr.Dropdown(choices=level_options, label="កម្រិតសិស្ស (TaRL Level)", value="Beginner")
+        grade = gr.Dropdown(grade_options, label="ថ្នាក់ (Grade)", value="1")
+        topic = gr.Dropdown(topic_options, label="ប្រធានបទ (Topic)", value="Addition")
+        level = gr.Dropdown(level_options, label="កម្រិតសិស្ស (TaRL Level)", value="Beginner")
 
     output_box = gr.Textbox(
         label="📘 Khmer Lesson Plan",