Update app.py

app.py

@@ -1,48 +1,27 @@
+import os
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from transformers import pipeline, AutoTokenizer
 import torch
 import spaces
-import datetime
-import json
-import os
-from datasets import Dataset
-from huggingface_hub import HfApi
-
-# ✅ Get token securely from environment
-token = os.environ.get("HF_TOKEN")
 
-# Dropdowns
+# Shared dropdown options
 grade_options = ["1", "2", "3", "4", "5", "6"]
 topic_options = ["Addition", "Subtraction", "Counting", "Number Recognition", "Multiplication", "Division"]
 level_options = ["Beginner", "Intermediate", "Advanced"]
 
-# ✅ 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 = {
-        "timestamp": timestamp,
-        "prompt": prompt,
-        "lesson": output
-    }
-
-    os.makedirs("history", exist_ok=True)
-    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)
-
-    dataset = Dataset.from_list([record])
-    dataset.push_to_hub(repo_id, token=token)
+# Load tokenizer separately so we can access eos_token_id
+HF_TOKEN = os.environ.get("HF_TOKEN")
+tokenizer = AutoTokenizer.from_pretrained("Pisethan/khmer-lesson-model", token=HF_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", use_auth_token=token)
     pipe = pipeline(
         "text-generation",
         model="Pisethan/khmer-lesson-model-v2",
         tokenizer=tokenizer,
         device=device,
-        use_auth_token=token
+        token=HF_TOKEN
     )
 
     prompt = f"""
@@ -62,57 +41,41 @@ TaRL Level: {level}
 """
 
     output = pipe(prompt, max_new_tokens=300, temperature=0.7, do_sample=True, eos_token_id=tokenizer.eos_token_id)
-
-    save_to_hf_dataset(prompt, output[0]["generated_text"])
-    return 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", use_auth_token=token)
     pipe = pipeline(
         "text-generation",
         model="Pisethan/khmer-lesson-model-v2",
         tokenizer=tokenizer,
         device=device,
-        use_auth_token=token
+        token=HF_TOKEN
     )
 
     results = ""
     for grade in grade_options:
         for topic in topic_options:
             for level in level_options:
-                prompt = f"""
-You are a lesson planning assistant. Return only one structured Khmer math lesson plan with these fields:
-
-Lesson Title:
-Objective:
-Activity:
-Instruction (Khmer):
-Materials:
-
-Please follow the structure exactly.
+                prompt = f"""Generate a Khmer math lesson plan.
 
 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)
-                lesson = output[0]["generated_text"]
-                results += f"🔹 ថ្នាក់ {grade} | {topic} | {level}\n{lesson}\n\n{'-'*50}\n\n"
-                save_to_hf_dataset(prompt, lesson)
-
+TaRL Level: {level}"""
+                output = pipe(prompt, max_new_tokens=200, temperature=0.7, do_sample=True)
+                results += f"🔹 ថ្នាក់ {grade} | {topic} | {level}\n{output[0]['generated_text']}\n\n{'-'*50}\n\n"
     return results
 
-# UI
+# Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("## 🤖 អ្នកជំនួយបង្កើតមេរៀនគណិតវិទ្យា")
     gr.Markdown("ជ្រើសរើសថ្នាក់ ប្រធានបទ និងកម្រិតសិស្ស រួចចុចបង្កើតមេរៀន។ ឬចុចប៊ូតុងខាងក្រោមសម្រាប់បង្កើតមេរៀនទាំងអស់។")
 
     with gr.Row():
-        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")
+        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")
 
     output_box = gr.Textbox(
         label="📘 Khmer Lesson Plan",