Update app.py

app.py

@@ -1,12 +1,11 @@
 import os
-import re
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
-from datasets import load_dataset
+import gradio as gr
 
+# Model name and Hugging Face token
 MODEL_NAME = "Pisethan/sangapac-math"
-
-# Get the token from the environment variable
 TOKEN = os.getenv("HF_API_TOKEN")
+
 if not TOKEN:
     raise ValueError("Hugging Face API token not found. Set it as an environment variable.")
 
@@ -19,48 +18,19 @@ except Exception as e:
     classifier = None
     print(f"Error loading model or tokenizer: {e}")
 
-# Load dataset dynamically from Hugging Face or locally
-try:
-    dataset = load_dataset("Pisethan/sangapac-math-dataset", token=TOKEN)["train"]
-    dataset_dict = {re.sub(r'\s+', ' ', entry["input"].strip()): entry for entry in dataset}
-except Exception as e:
-    dataset_dict = {}
-    print(f"Error loading dataset: {e}")
-
-def normalize_input(text):
-    return re.sub(r'\s+', ' ', text.strip())
-
 def predict(input_text):
     if classifier is None:
         return "Model not loaded properly.", {"Error": "Model not loaded properly."}
 
     try:
-        input_text = normalize_input(input_text)
         result = classifier(input_text)
         label = result[0]["label"]
         score = result[0]["score"]
 
-        data = dataset_dict.get(input_text, {"output": "Unknown", "metadata": {}})
-        output = data["output"]
-        metadata = data["metadata"]
-
-        difficulty = metadata.get("difficulty", "Unknown")
-        steps = metadata.get("steps", ["No steps available"])
-
-        steps_text = "\n".join(steps)
-        simple_result = (
-            f"Category: {label}\n"
-            f"Confidence: {score:.2f}\n"
-            f"Result: {output}\n"
-            f"Difficulty: {difficulty}\n"
-            f"Steps:\n{steps_text}"
-        )
-
+        simple_result = f"Category: {label}\nConfidence: {score:.2f}"
         detailed_result = {
             "Category": label,
             "Confidence": score,
-            "Output (Result)": output,
-            "Metadata": metadata,
         }
 
         return simple_result, detailed_result
@@ -68,8 +38,6 @@ def predict(input_text):
         return "An error occurred.", {"Error": str(e)}
 
 # Gradio interface
-import gradio as gr
-
 sample_inputs = [
     ["1 + 1 = ?"],
     ["(5 + 3) × 2 = ?"],