Update app.py

app.py

@@ -3,15 +3,16 @@ from transformers import pipeline
 from datasets import load_dataset
 
 # Load the dataset
-dataset = load_dataset("ziq/depression_advice")
+advice_dataset = load_dataset("ziq/depression_advice")
+depression_dataset = load_dataset("ShreyaR/DepressionDetection")
 
 # Load the model
 model_name = "mrm8488/distilroberta-base-finetuned-suicide-depression"
 qa_pipeline = pipeline("question-answering", model=model_name)
 
 # Extract context and messages
-contexts = dataset["train"]["context"]
-messages = dataset["train"]["text"]
+contexts = advice_dataset["train"]["text"]
+messages = depression_dataset["train"]["text"]
 
 # Define a function to generate answers
 def generate_answer(messages):
@@ -19,7 +20,7 @@ def generate_answer(messages):
     results = qa_pipeline(question=messages, context=contexts)
     
     # Return the first answer
-    return results["answer"][0] if results["answer"] else "No answer found."
+    return results["answer"] if results["answer"] else "No answer found."
 
 # Create a Gradio interface
 iface = gr.Interface(