Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import os  # For environment variables
-import time  # To simulate processing time if needed
+import os 
+from sympy import symbols, Eq, solve, latex
 
 # Initialize the Hugging Face Inference Client
 client = InferenceClient()
@@ -9,26 +9,28 @@ client = InferenceClient()
 
 
 def preprocess_latex(content):
-    # Split content into lines for better formatting
+    # Example: Split the response into meaningful parts
     lines = content.split("\n")
-    formatted_lines = []
-    
+    processed_lines = []
+
     for line in lines:
-        # If a line contains equations or math expressions, wrap them properly
-        if "Simplify" in line or "Solve" in line or "boxed" in line or "frac" in line:
-            formatted_lines.append(f"$$ {line.strip()} $$")  # Block math
-        elif "(" in line and ")" in line:  # Inline math for variables
-            formatted_lines.append(line.replace("(", "$").replace(")", "$"))
+        if "[" in line and "]" in line:  # Treat as a mathematical expression
+            math_expr = line.strip("[]")
+            try:
+                # Parse and convert to LaTeX
+                processed_lines.append(f"$$ {math_expr} $$")
+            except:
+                processed_lines.append(line)  # Fallback to raw line if parsing fails
+        elif "(" in line and ")" in line:  # Treat as inline math
+            processed_lines.append(line.replace("(", "$").replace(")", "$"))
         else:
-            formatted_lines.append(line)  # Plain text
+            processed_lines.append(line)  # Plain text remains as-is
 
-    # Join lines back into a single string
-    return "\n".join(formatted_lines)
+    return "\n".join(processed_lines)
 
 # Function to generate and format AI response
 def generate_response(prompt_template, **kwargs):
     # Simulate processing/loading
-    time.sleep(1)  # Optional: Remove or adjust based on actual execution time
     prompt = os.getenv(prompt_template).format(**kwargs)
     response = client.chat.completions.create(
         model="Qwen/Qwen2.5-Math-1.5B-Instruct",