Fixed formatting errors:

app.py

@@ -334,30 +334,28 @@ def build_interface():
     """
     with gr.Blocks() as demo:
         gr.Markdown("""
-    # Applicant Task: Target Company & Law Firm Identification
+# Applicant Task: Target Company & Law Firm Identification
 
-    ---
+This task involves processing a user query to determine the relevance to the intended task, followed by analyzing textual data  to extract information about law firms representing parties (Buyer, Seller, and Third Parties) and verifying the presence of a target company. You can refer to the following legal document: [SEC Agreement Example](https://www.sec.gov/Archives/edgar/data/28452/000119312505012401/dex101.htm)
 
-    This task involves processing a user query to determine the relevance to the intended task, followed by analyzing textual data  to extract information about law firms representing parties (Buyer, Seller, and Third Parties) and verifying the presence of a target company. You can refer to the following legal document: [SEC Agreement Example](https://www.sec.gov/Archives/edgar/data/28452/000119312505012401/dex101.html)
+The system is designed to sequentially leverage three LLM functions:
 
-    The system is designed to sequentially leverage three LLM functions:
+### Step 1: LLM1
+- Determines if the user's query mentions any target company.
+- If no target company is found, LLM1 responds with a message wrapped in `<user_message></user_message>` XML tags to inform the user that the query is irrelevant to this task.
+- If the query contains a target company, LLM1 moves forward with a formatted acknowledgment of the identified target company.
 
-    ### Step 1: LLM1
-    - Determines if the user's query mentions any target company.
-    - If no target company is found, LLM1 responds with a message wrapped in `<user_message></user_message>` XML tags to inform the user that the query is irrelevant to this task.
-    - If the query contains a target company, LLM1 moves forward with a formatted acknowledgment of the identified target company.
+### Step 2: LLM2
+- Examines four separate paragraphs independently.
+- For each paragraph, extracts:
+    - Buyer's representative law firm
+    - Seller's representative law firm
+    - Any third-party law firm present
+    - Whether the target company is mentioned in the paragraph
+- Each paragraph's results are formatted and concatenated for the next step.
 
-    ### Step 2: LLM2
-    - Examines four separate paragraphs independently.
-    - For each paragraph, extracts:
-        - Buyer's representative law firm
-        - Seller's representative law firm
-        - Any third-party law firm present
-        - Whether the target company is mentioned in the paragraph
-    - Each paragraph's results are formatted and concatenated for the next step.
-
-    ### Step 3: LLM3
-    - Compiles the information from all analyzed paragraphs and outputs a structured JSON object:
+### Step 3: LLM3
+- Compiles the information from all analyzed paragraphs and outputs a structured JSON object:
 
 ```json
 {