Updated app.py per BOLT's instructions

app.py

@@ -6,69 +6,58 @@ app = Flask(__name__)
 
 # Load Markdown files
 def loadMarkdownFiles(directory):
-  const files = fs.readdirSync(directory);
-  const markdownFiles = [];
+  files = os.listdir(directory)
+  markdownFiles = []
 
-  for (const file of files) {
-    if (path.extname(file).toLowerCase() === '.md') {
-      const filePath = path.join(directory, file);
-      const content = fs.readFileSync(filePath, 'utf-8');
-      markdownFiles.push({ name: file, content });
-    }
-  }
+  for file in files:
+    if file.endswith('.md'):
+      filePath = os.path.join(directory, file)
+      with open(filePath, 'r', encoding='utf-8') as f:
+        content = f.read()
+        markdownFiles.append({'name': file, 'content': content})
 
   return markdownFiles
 
 # Initialize RAG model and tokenizer
 async def initRagModel():
-  const tokenizer = await AutoTokenizer.from_pretrained('facebook/rag-token-nq');
-  const model = await RagTokenForGeneration.from_pretrained('facebook/rag-token-nq');
+  from transformers import AutoTokenizer, RagTokenForGeneration
+  tokenizer = await AutoTokenizer.from_pretrained('facebook/rag-token-nq')
+  model = await RagTokenForGeneration.from_pretrained('facebook/rag-token-nq')
 
-  return { tokenizer, model }
+  return {'tokenizer': tokenizer, 'model': model}
 
 # Retrieve relevant information from Markdown files using Ollama API
 async def retrieveInformation(query):
   try:
-    response = await axios.post('http://localhost:8080/chat', { query });
-    return response.data.response;
+    config = configparser.ConfigParser()
+    config.read('ollama.ini')
+    host = config.get('Ollama', 'host')
+    port = config.get('Ollama', 'port')
+
+    response = await axios.post(f'http://{host}:{port}/chat', {'query': query})
+    return response.json()['response']
   except (axios.AxiosError, Exception) as error:
-    print(f'Error: {error.message}')
+    print(f'Error: {error}')
     raise
 
 # Chatbot logic
 async def chatbot():
-  const directory = './notes'; // Directory containing Markdown files
-  const markdownFiles = loadMarkdownFiles(directory);
-  const ragModel = await initRagModel();
+  directory = './notes'  # Directory containing Markdown files
+  markdownFiles = loadMarkdownFiles(directory)
+  ragModel = await initRagModel()
 
   print('Chatbot is ready! Ask your questions.')
 
-  process.stdin.on('data', async (data) => {
-    const query = data.toString().trim();
-    if (query.toLowerCase() === 'exit') {
-      process.exit(0);
-    }
-
-    try:
-      const response = await retrieveInformation(query);
-      print(f'Chatbot: {response}')
-    except Exception as error:
-      print(f'Error: {error.message}')
-
-# Flask route to handle chat requests
-@app.route('/chat', methods=['POST'])
-async def chat():
-  data = request.json
-  query = data.get('query')
-
-  if not query:
-    return jsonify({'error': 'No query provided'}), 400
+  while True:
+    query = input().strip()
+    if query.lower() == 'exit':
+      break
 
     try:
       response = await retrieveInformation(query)
-    return jsonify({'response': response})
+      print(f'Chatbot: {response}')
     except Exception as error:
-    return jsonify({'error': str(error)}), 500
+      print(f'Error: {error}')
 
 if __name__ == '__main__':
   asyncio.run(chatbot())