Create tool.py

tool.py

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+from gradio_client import Client
+import os
+import json
+
+# Function to load question sets from a directory
+def load_question_sets_vce(directory='questions'):
+    question_sets = []
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            if file.endswith(".json"):
+                question_sets.append(os.path.join( file)[:-5])  # remove the .json extension
+    return question_sets
+
+exams = load_question_sets_vce('questions/')
+print("question_sets:", exams)
+
+def select_exam_vce(exam_name):
+    file_path = os.path.join(os.getcwd(), 'questions', f'{exam_name}.json')
+    try:
+        with open(file_path, 'r') as f:
+            questions = json.load(f)
+            print(f"Loaded {len(questions)} questions")
+            return questions  # Ensure the questions are returned here
+    except FileNotFoundError:
+        print(f"File {file_path} not found.")
+        return []  # Return an empty list to indicate no questions were found
+
+
+# Text-to-speech function with rate limiting, retry mechanism, and client rotation
+import time
+import httpx
+# Text-to-speech clients 
+client_1 = Client("ruslanmv/text-to-speech-fast")
+client_2 = Client("ruslanmv/Text-To-Speech")
+client_3 = Client("ruslanmv/Text-to-Voice-Transformers")
+clients = [client_1, client_2, client_3]
+# Text-to-speech function with rate limiting, retry mechanism, and client rotation
+def text_to_speech(text, retries=3, delay=5):
+    client_index = 0  # Start with the first client
+    for attempt in range(retries):
+        try:
+            client = clients[client_index]
+            print(f"Attempt {attempt + 1}")
+            if client_index == 0:
+                result = client.predict(
+                    language="English",
+                    repo_id="csukuangfj/vits-piper-en_US-hfc_female-medium|1 speaker",
+                    text=text,
+                    sid="0",
+                    speed=0.8,
+                    api_name="/process"
+                )
+            else:
+                result = client.predict(
+                    text=text,
+                    api_name="/predict"
+                )
+            return result
+        except httpx.HTTPStatusError as e:
+            if e.response.status_code == 429:
+                print(f"Rate limit exceeded. Retrying in {delay} seconds...")
+                client_index = (client_index + 1) % len(clients)  # Rotate to the next client
+                time.sleep(delay)
+            else:
+                raise e
+
+    print("Max retries exceeded. Could not process the request.")
+    return None