Update app.py

app.py

@@ -31,7 +31,7 @@ def download_pdf(url):
 def custom_chunking(text, delimiter="\n\n"):
     """Splits text based on a specified delimiter."""
     return text.split(delimiter)
-
+    
 def extract_text_from_pdf(pdf_bytes, document_id):
     """Extracts text from a PDF, page by page, and then chunks each page."""
     pdf_file = io.BytesIO(pdf_bytes)
@@ -147,7 +147,7 @@ def calculate_confidence(query, context, answer):
     query_embedding = embedding_model.encode([query], convert_to_numpy=True)
     context_embedding = embedding_model.encode([context], convert_to_numpy=True)
     answer_embedding = embedding_model.encode([answer], convert_to_numpy=True)
-
+    
     query_context_similarity = np.dot(query_embedding, context_embedding.T).item()
     context_answer_similarity = np.dot(context_embedding, answer_embedding.T).item()
     confidence = (query_context_similarity + context_answer_similarity) / 2.0 # Equal weights
@@ -161,7 +161,7 @@ def generate_response(query, context):
         - JUST PROVIDE ONLY THE ANSWER.
         - Provide a elaborate, factual answer based strictly on the Context.
         - Avoid generating Python code, solutions, or any irrelevant information.
-    Context: {context}
+    Context: {context}  
     Question: {query}
     Answer:"""
     response = generator(prompt, max_new_tokens=500, num_return_sequences=1)[0]['generated_text']
@@ -256,4 +256,4 @@ with gr.Blocks() as demo:
 
     iface.render()
 
-demo.launch()
+demo.launch()