Update app.py

app.py

@@ -1,12 +1,9 @@
 import os
-import time
 import gradio as gr
 from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
 from llama_index.embeddings.mixedbreadai import MixedbreadAIEmbedding
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.llms.groq import Groq
 from llama_parse import LlamaParse
-from mixedbread_ai.core.api_error import ApiError
 
 # API keys
 llama_cloud_key = os.environ.get("LLAMA_CLOUD_API_KEY")
@@ -18,7 +15,6 @@ if not (llama_cloud_key and groq_key and mxbai_key):
 # Model names
 llm_model_name = "llama-3.1-70b-versatile"
 embed_model_name = "mixedbread-ai/mxbai-embed-large-v1"
-fallback_embed_model = "sentence-transformers/all-MiniLM-L6-v2"  # Fallback model
 
 # Initialize the parser
 parser = LlamaParse(api_key=llama_cloud_key, result_type="markdown")
@@ -41,25 +37,8 @@ file_extractor = {
 }
 
 # Initialize models with error handling
-def initialize_embed_model(max_retries=3, delay=2):
-    for attempt in range(max_retries):
-        try:
-            return MixedbreadAIEmbedding(api_key=mxbai_key, model_name=embed_model_name)
-        except ApiError as e:
-            if attempt == max_retries - 1:
-                print(f"Failed to initialize Mixedbread AI embedding after {max_retries} attempts: {str(e)}")
-                print("Falling back to local HuggingFace embedding model.")
-                return HuggingFaceEmbedding(model_name=fallback_embed_model)
-            time.sleep(delay)
-        except Exception as e:
-            print(f"Unexpected error initializing embedding model: {str(e)}")
-            if attempt == max_retries - 1:
-                print("Falling back to local HuggingFace embedding model.")
-                return HuggingFaceEmbedding(model_name=fallback_embed_model)
-            time.sleep(delay)
-
 try:
-    embed_model = initialize_embed_model()
+    embed_model = MixedbreadAIEmbedding(api_key=mxbai_key, model_name=embed_model_name)
     llm = Groq(model=llm_model_name, api_key=groq_key)
 except Exception as e:
     raise RuntimeError(f"Failed to initialize models: {str(e)}")
@@ -68,7 +47,7 @@ except Exception as e:
 vector_index = None
 
 # File processing function
-def load_files(file_path: str, max_retries=3, delay=2):
+def load_files(file_path: str):
     global vector_index
     if not file_path:
         return "No file path provided. Please upload a file."
@@ -82,25 +61,14 @@ def load_files(file_path: str, max_retries=3, delay=2):
             input_files=[file_path], 
             file_extractor=file_extractor
         ).load_data()
-        
-        # Retry logic for creating vector index
-        for attempt in range(max_retries):
-            try:
-                vector_index = VectorStoreIndex.from_documents(
-                    document, 
-                    embed_model=embed_model
-                )
-                filename = os.path.basename(file_path)
-                return f"Ready to provide responses based on: {filename}"
-            except ApiError as e:
-                if attempt == max_retries - 1:
-                    return f"Error processing file after {max_retries} attempts: {str(e)}"
-                print(f"Attempt {attempt + 1} failed: {str(e)}. Retrying in {delay} seconds...")
-                time.sleep(delay)
-            except Exception as e:
-                return f"Unexpected error processing file: {str(e)}"
+        vector_index = VectorStoreIndex.from_documents(
+            document, 
+            embed_model=embed_model
+        )
+        filename = os.path.basename(file_path)
+        return f"Ready to provide responses based on: {filename}"
     except Exception as e:
-        return f"Error loading file: {str(e)}"
+        return f"Error processing file: {str(e)}"
 
 # Respond function
 def respond(message, history):
@@ -147,7 +115,7 @@ with gr.Blocks(
         with gr.Column(scale=3):
             chatbot = gr.ChatInterface(
                 fn=respond,
-                chatbot=gr.Chatbot(height=300, type="messages"),
+                chatbot=gr.Chatbot(height=300, type="messages"),  # Fixed deprecated warning
                 theme="soft",
                 show_progress="full",
                 textbox=gr.Textbox(