Update app.py

app.py

@@ -107,23 +107,23 @@ def upload_document(file):
 
     return "Document uploaded and indexed successfully."
 
+
 # ===============================
-# GENERATION PIPELINE 
+# QA GENERATION PIPELINE
 # ===============================
-query_vector = get_embeddings(query, is_query=True).astype("float32")
+# Initialize text generation pipeline (you can use a more powerful model if needed)
+qa_pipeline = pipeline("text-generation", model="gpt2")
 
 def generate_answer_from_file(query, top_k=10):
     if not document_texts:
         return "No documents indexed yet."
 
-    query_vector = get_embeddings(query).astype("float32")
+    query_vector = get_embeddings(query, is_query=True).astype("float32")
     scores, indices = index.search(query_vector, k=top_k)
     retrieved_chunks = [document_texts[i] for i in indices[0]]
     context = "\n\n".join(retrieved_chunks)
 
-    print("\n--- Retrieved Context ---\n", context)  # Debugging print
-
-    # Prompt Engineering
+    # Prompt for the model
     prompt = (
         "You are a helpful assistant reading student notes or textbook passages.\n\n"
         "Based on the context provided, answer the question accurately and clearly.\n\n"
@@ -140,6 +140,7 @@ def generate_answer_from_file(query, top_k=10):
     result = qa_pipeline(prompt, max_length=512, do_sample=False)[0]['generated_text']
     return result.strip()
 
+
 # ===============================
 # GRADIO INTERFACES
 # ===============================
@@ -161,5 +162,3 @@ search_interface = gr.Interface(
 
 app = gr.TabbedInterface([upload_interface, search_interface], ["Upload", "Ask"])
 app.launch()
-
-