Update app.py

app.py

@@ -1,5 +1,5 @@
-import gradio as gr
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from datasets import load_dataset
 
 # Model details
 MODEL_NAME = "Pisethan/sangapac-math"
@@ -13,6 +13,15 @@ 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")["train"]  # Load your dataset
+    dataset_dict = {entry["input"]: entry for entry in dataset}  # Create a dictionary for lookup
+except Exception as e:
+    dataset_dict = {}
+    print(f"Error loading dataset: {e}")
+
+
 def predict(input_text):
     if classifier is None:
         return {"Error": "Model not loaded properly."}
@@ -20,17 +29,26 @@ def predict(input_text):
     try:
         # Predict the category
         result = classifier(input_text)
-        label = result[0]["label"]  # Directly use the label provided by the model
+        label = result[0]["label"]
         score = result[0]["score"]
 
+        # Retrieve output and metadata dynamically from the dataset
+        data = dataset_dict.get(input_text, {"output": "Unknown", "metadata": {}})
+        output = data["output"]
+        metadata = data["metadata"]
+
         return {
-            "Category": label,  # No need to remap since the label is already readable
+            "Category": label,
             "Confidence": score,
+            "Output (Result)": output,
+            "Metadata": metadata,
         }
     except Exception as e:
         return {"Error": str(e)}
 
 # Gradio interface
+import gradio as gr
+
 interface = gr.Interface(
     fn=predict,
     inputs=gr.Textbox(lines=2, placeholder="Enter a math problem..."),