Updated the description

app.py

@@ -11,6 +11,9 @@ SCOPES = [
     "https://www.googleapis.com/auth/drive"
 ]
 
+MODEL_NAME = "gpt-4o-mini"  # Ensure this matches your deployed model.
+TEMPERATURE = 0.2
+
 # Initialize the OpenAI client with the API key from environment variables.
 client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
 
@@ -153,11 +156,12 @@ def submit_prompt(email, name, system_prompt_1, system_prompt_2, system_prompt_3
         expected = item["expected"]
         try:
             response = client.chat.completions.create(
-                model="gpt-4o-mini",  # Ensure this model identifier matches your deployed model.
+                model=MODEL_NAME,  # Ensure this model identifier matches your deployed model.
                 messages=[
                     {"role": "system", "content": system_prompt_1},
                     {"role": "user", "content": question}
-                ]
+                ],
+                temperature=TEMPERATURE
             )
             output_1 = response.choices[0].message.content.strip()
         except Exception as e:
@@ -179,11 +183,12 @@ def submit_prompt(email, name, system_prompt_1, system_prompt_2, system_prompt_3
             for doc in docs:
                 try:
                     response = client.chat.completions.create(
-                        model="gpt-4o-mini",
+                        model=MODEL_NAME,
                         messages=[
                             {"role": "system", "content": system_prompt_2},
                             {"role": "user", "content": f"Target company context: \n{output1} \n\n Paragraph:\n {doc}"}
-                        ]
+                        ],
+                        temperature=TEMPERATURE
                     )
                     output2 += "\n" + response.choices[0].message.content.strip()
                 except Exception as e:
@@ -194,11 +199,12 @@ def submit_prompt(email, name, system_prompt_1, system_prompt_2, system_prompt_3
             output2 = output2.strip()
             try:
                 response = client.chat.completions.create(
-                    model="gpt-4o-mini",
+                    model=MODEL_NAME,
                     messages=[
                         {"role": "system", "content": system_prompt_3},
                         {"role": "user", "content": f"Extracted information: \n{output2}"}
-                    ]
+                    ],
+                    temperature=TEMPERATURE,
                 )
                 answer = response.choices[0].message.content.strip()
             except Exception as e:
@@ -328,34 +334,30 @@ def build_interface():
     """
     with gr.Blocks() as demo:
         gr.Markdown("""
-# Applicant Task: Target Company & Law Firm Identification
-
----
+    # Applicant Task: Target Company & Law Firm Identification
 
-## Identifying Parties, Law Firms, and Target Company Presence
+    ---
 
----
-
-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.
+    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
 {
@@ -366,6 +368,13 @@ The system is designed to sequentially leverage three LLM functions:
 }
 ```
 
+| Field                  | Default Value if Missing | Type      |
+| ---------------------- | ------------------------ | --------- |
+| `buyer_firm`           | `"null"`                 | `string`  |
+| `seller_firm`          | `"null"`                 | `string`  |
+| `third_party`          | `"null"`                 | `string`  |
+| `contains_target_firm` | `false`                  | `boolean` |
+
 The goal is to identify the representative law firms of involved parties and determine if the target company is mentioned, ensuring the results are structured and accurate.
 
 ---