From c8efef8f04c08821657dc5427b7b21354d058ff7 Mon Sep 17 00:00:00 2001
From: ManishMadan2882 <manishmadan321@gmail.com>
Date: Fri, 18 Apr 2025 18:27:02 +0530
Subject: [PATCH] (fix:openai) image uplads, use lambda in process_files

---
 application/llm/openai.py    | 23 +++++++++--------------
 application/storage/local.py |  2 +-
 application/storage/s3.py    | 10 +++++-----
 3 files changed, 15 insertions(+), 20 deletions(-)

diff --git a/application/llm/openai.py b/application/llm/openai.py
index f36e87cb..87eb295b 100644
--- a/application/llm/openai.py
+++ b/application/llm/openai.py
@@ -1,7 +1,5 @@
 import json
 import base64
-import os
-import mimetypes
 import logging
 
 from application.core.settings import settings
@@ -79,6 +77,8 @@ class OpenAILLM(BaseLLM):
                                 content_parts.append(item)
                             elif "type" in item and item["type"] == "file" and "file" in item:
                                 content_parts.append(item)
+                            elif "type" in item and item["type"] == "image_url" and "image_url" in item:
+                                content_parts.append(item)
                             cleaned_messages.append({"role": role, "content": content_parts})
                         else:
                             raise ValueError(
@@ -223,7 +223,6 @@ class OpenAILLM(BaseLLM):
             elif mime_type == 'application/pdf':
                 try:
                     file_id = self._upload_file_to_openai(attachment)
-
                     prepared_messages[user_message_index]["content"].append({
                         "type": "file",
                         "file": {"file_id": file_id}
@@ -282,17 +281,13 @@ class OpenAILLM(BaseLLM):
             raise FileNotFoundError(f"File not found: {file_path}")
 
         try:
-            # Use storage's process_file method to handle the file appropriately
-            def upload_to_openai(file_path, **kwargs):
-                with open(file_path, 'rb') as file:
-                    logging.info(f"Uploading file to OpenAI: {file_path}")
-                    response = self.client.files.create(
-                        file=file,
-                        purpose="assistants"
-                    )
-                    return response.id
-
-            file_id = self.storage.process_file(file_path, upload_to_openai)
+            file_id = self.storage.process_file(
+                file_path,
+                lambda local_path, **kwargs: self.client.files.create(
+                    file=open(local_path, 'rb'),
+                    purpose="assistants"
+                ).id
+            )
 
             from application.core.mongo_db import MongoDB
             mongo = MongoDB.get_client()
diff --git a/application/storage/local.py b/application/storage/local.py
index db11b63c..fb21f08d 100644
--- a/application/storage/local.py
+++ b/application/storage/local.py
@@ -100,4 +100,4 @@ class LocalStorage(BaseStorage):
         if not os.path.exists(full_path):
             raise FileNotFoundError(f"File not found: {full_path}")
 
-        return processor_func(file_path=full_path, **kwargs)
+        return processor_func(local_path=full_path, **kwargs)
diff --git a/application/storage/s3.py b/application/storage/s3.py
index e8df210e..abc57c6d 100644
--- a/application/storage/s3.py
+++ b/application/storage/s3.py
@@ -98,23 +98,23 @@ class S3Storage(BaseStorage):
             path: Path to the file
             processor_func: Function that processes the file
             **kwargs: Additional arguments to pass to the processor function
-
+        
         Returns:
             The result of the processor function
         """
         import tempfile
         import logging
-
+        
         if not self.file_exists(path):
             raise FileNotFoundError(f"File not found in S3: {path}")
-
+        
         with tempfile.NamedTemporaryFile(suffix=os.path.splitext(path)[1], delete=True) as temp_file:
             try:
                 # Download the file from S3 to the temporary file
                 self.s3.download_fileobj(self.bucket_name, path, temp_file)
                 temp_file.flush()
-                result = processor_func(file_path=temp_file.name, **kwargs)
-                return result
+                
+                return processor_func(local_path=temp_file.name, **kwargs)
             except Exception as e:
                 logging.error(f"Error processing S3 file {path}: {e}", exc_info=True)
                 raise