Modify description

app.py

@@ -90,26 +90,31 @@ def response(user_schemas, user_question):
         outputs = model.generate(**inputs, max_new_tokens=1024)
     
     outputs = tokenizer.batch_decode(outputs)
-    output = outputs[0].split("<|im_start|>assistant")[-1]
+    output = outputs[0].split("<|im_start|>assistant")[-1].strip()
 
     final_answer = extract_answer(output)
 
-    return output + "\n\n" + "="*20 + "\n\nFinal Answer: \n" + final_answer
+    final_output = output + "\n\n" + "="*20 + "\n\nFinal Answer: \n" + final_answer
+
+    return final_output
 
 
 desc="""
-Please use the "Table Schemas" field to provide the required schemas to to generate the SQL Query for - separated by new lines.
-Eg. CREATE TABLE demographic (
-        subject_id text, 
-        admission_type text, 
-        hadm_id text)
-
-    CREATE TABLE diagnoses (
-        subject_id text,
-        hadm_id text)
-
-Finally, use the "Question" field to provide the relevant question to be answered based on the provided schemas.
-Eg. How many patients whose admission type is emergency.
+Please use the "Table Schemas" field to provide the required schemas to to generate the SQL Query for - separated by new lines.<br>
+**Example:**
+```python
+CREATE TABLE demographic (
+    subject_id text, 
+    admission_type text, 
+    hadm_id text)
+
+CREATE TABLE diagnoses (
+    subject_id text,
+    hadm_id text)
+```
+
+Finally, use the "Question" field to provide the relevant question to be answered based on the provided schemas.<br>
+**Example:** How many patients whose admission type is emergency.
 """
 
 demo = gr.Interface(