Update app.py

app.py

@@ -14,13 +14,17 @@ model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever
 inputs = tokenizer(contexts, return_tensors='pt', padding=True, truncation=True)
 labels = tokenizer(responses, return_tensors='pt', padding=True, truncation=True)
 
+# Extract the abstracts
+abstracts = df['Abstract'].dropna().tolist()
+
 # Load your dataset
 df = pd.read_csv('10kstats.csv')
 
-# Ensure the dataset has the required columns for RAG
-# For example, it should have 'context' and 'response' columns
-contexts = df['Abstract'].tolist()
-#responses = df['response'].tolist()
+# Generate context-response pairs (abstract-question pairs)
+# Here we use the abstracts as contexts and simulate questions
+
+contexts = abstracts
+responses = ["Can you tell me more about this research?" for _ in abstracts]
 
 @spaces.GPU
 def generate_response(input_text):