Fix nbfmt

Author: tiffc

TxGemma/[TxGemma]Agentic_Demo_with_Hugging_Face.ipynb

@@ -613,7 +613,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-	    "Instructions: Answer the following question about drug properties.\n",
+            "Instructions: Answer the following question about drug properties.\n",
             "Context: As a membrane separating circulating blood and brain extracellular fluid, the blood-brain barrier (BBB) is the protection layer that blocks most foreign drugs. Thus the ability of a drug to penetrate the barrier to deliver to the site of action forms a crucial challenge in development of drugs for central nervous system.\n",
             "Question: Given a drug SMILES string, predict whether it\n",
             "(A) does not cross the BBB (B) crosses the BBB\n",
@@ -636,17 +636,17 @@
         "def txgemma_predict(prompt):\n",
         "    input_ids = predict_tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
         "    outputs = predict_model.generate(**input_ids, max_new_tokens=8)\n",
-	"    return predict_tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)\n",
+        "    return predict_tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)\n",
         "\n",
         "def txgemma_chat(prompt):\n",
-	"    messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-	"    inputs = chat_tokenizer.apply_chat_template(\n",
-	"        messages, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\"\n",
-	"    ).to(\"cuda\")\n",
+        "    messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+        "    inputs = chat_tokenizer.apply_chat_template(\n",
+        "        messages, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\"\n",
+        "    ).to(\"cuda\")\n",
         "    outputs = chat_model.generate(input_ids=inputs, max_new_tokens=200)\n",
         "    return chat_tokenizer.decode(outputs[0, len(inputs[0]):], skip_special_tokens=True)\n",
         "\n",
-	"print(TDC_PROMPT)\n",
+        "print(TDC_PROMPT)\n",
         "print(f\"\\nPrediction model response: {txgemma_predict(TDC_PROMPT)}\")\n",
         "if USE_CHAT: print(f\"\\nChat model response: {txgemma_chat(TDC_PROMPT)}\")"
       ]
@@ -1251,7 +1251,7 @@
             "**Conclusion:**\n",
             "\n",
             "For a drug to be considered for broad development, a favorable safety and toxicity profile is paramount. **Aspirin (Drug 1)** has a demonstrably superior safety profile compared to Vorinostat (Drug 2). Its wide range of proven therapeutic benefits and lower toxicity make it the unequivocally preferred candidate for further development over a compound with predicted toxicity and a more niche, high-risk therapeutic application.\n"
-	  ]
+          ]
         }
       ],
       "source": [

TxGemma/[TxGemma]Quickstart_with_Model_Garden.ipynb

@@ -68,7 +68,7 @@
         "\n",
         "- Deploy TxGemma to a Vertex AI Endpoint and get online predictions.\n",
         "- Upload TxGemma to Vertex AI Model Registry and get batch predictions.\n",
-	"\n",
+        "\n",
         "### Costs\n",
         "\n",
         "This tutorial uses billable components of Google Cloud:\n",