Update app.py

app.py

@@ -1659,8 +1659,7 @@ def clean_content(raw_content: str) -> str:
 def display_image():
     return "visual.png"
 
-def openai_call(prompt: str, messages: list = None, model: str = "o3-mini",
-                temperature: float = 0.0, max_tokens_param: int = 10000) -> str:
+def openai_call(prompt: str, messages: list = None, model: str = "o3-mini", temperature: float = 0.0, max_tokens_param: int = 10000) -> str:
     if messages is None or len(messages) == 0:
         messages = [{"role": "user", "content": prompt}]
     if len(messages[0]['content']) > MAX_MESSAGE_LENGTH:
@@ -1671,12 +1670,11 @@ def openai_call(prompt: str, messages: list = None, model: str = "o3-mini",
             "model": model,
             "messages": messages
         }
-        # Note: Adjust token names based on the model
         if model == "o3-mini":
             params["max_completion_tokens"] = max_tokens_param
         else:
             params["max_tokens"] = max_tokens_param
-            params["temperature"]: temperature
+            params["temperature"]= temperature
         response = client.chat.completions.create(**params)
         result = response.choices[0].message.content
         result = result.strip().strip("json").strip("```").strip()
@@ -2293,18 +2291,19 @@ def generate_query_tree(context: str, breadth: int, depth: int, selected_engines
     list_engines = "all relevant search engines" if selected_engines is None else ','.join(map(str, selected_engines))
     
     prompt = f"""
+Generate a list of {breadth} search queries relevant to the following context:
 
-    Generate a list of {breadth} search queries relevant to the following context:
-
-    "{context}"
+"{context}"
 
 // Requirements
 - The queries should be suitable for a search engine.
 - Each query should combine terms using logical operators (AND, OR) where appropriate.
 - Do not include explanations or introductory phrases,
-- just output a JSON object containing a list of strings named 'queries'.
+- Just output a JSON object containing a list of strings named 'queries'.
+
 // IMPORTANT:
 - Return only valid JSON without any markdown code fences (```) or mention of json
+
 // EXAMPLE (if breadth = 4):
     {{
       "queries": [
@@ -2314,11 +2313,12 @@ def generate_query_tree(context: str, breadth: int, depth: int, selected_engines
         "Statistics" AND "data analysis" AND "machine learning algorithms"
       ]
     }}
+
 Do not include any extra text, markdown formatting, or commentary. Output the JSON starting from "{{" and ending with "}}".
 Now generate the result.
 """
     messages = []
-    llm_response = openai_call(prompt=prompt, messages=messages, model="o3-mini", temperature=0, max_tokens_param=150)
+    llm_response = openai_call(prompt=prompt, messages=messages, model="o3-mini", temperature=0, max_tokens_param=800)
     logging.info(f"Generated query tree: {llm_response}")
 
     cleaned_response = llm_response.strip().strip("`").strip()