fixed aws errors

app.py

@@ -1,261 +1,4 @@
-# import streamlit as st
-# import os
-# from PyPDF2 import PdfReader
-# import pymupdf
-# import numpy as np
-# import cv2
-# import shutil
-# import imageio
-# from PIL import Image
-# import imagehash
-# import matplotlib.pyplot as plt
-# from llama_index.core.indices import MultiModalVectorStoreIndex
-# from llama_index.vector_stores.qdrant import QdrantVectorStore
-# from llama_index.core import SimpleDirectoryReader, StorageContext
-# import qdrant_client
-# from llama_index.core import PromptTemplate
-# from llama_index.core.query_engine import SimpleMultiModalQueryEngine
-# from llama_index.llms.openai import OpenAI
-# from llama_index.core import load_index_from_storage, get_response_synthesizer
-# import tempfile
-# from qdrant_client import QdrantClient, models
-# import getpass
-
-# curr_user = getpass.getuser()
-# # from langchain.vectorstores import Chroma
-# # To connect to the same event-loop,
-# # allows async events to run on notebook
-
-# # import nest_asyncio
-
-# # nest_asyncio.apply()
-
-# from dotenv import load_dotenv
-# load_dotenv()
-
-
-# def extract_text_from_pdf(pdf_path):
-#     reader = PdfReader(pdf_path)
-#     full_text = ''
-#     for page in reader.pages:
-#         text = page.extract_text()
-#         full_text += text
-#     return full_text
-
-# def extract_images_from_pdf(pdf_path, img_save_path):
-#     doc = pymupdf.open(pdf_path)
-#     for page in doc:
-#         img_number = 0
-#         for block in page.get_text("dict")["blocks"]:
-#             if block['type'] == 1:
-#                 name = os.path.join(img_save_path, f"img{page.number}-{img_number}.{block['ext']}")
-#                 out = open(name, "wb")
-#                 out.write(block["image"])
-#                 out.close()
-#                 img_number += 1
-
-# def is_empty(img_path):
-#     image = cv2.imread(img_path, 0)
-#     std_dev = np.std(image)
-#     return std_dev < 1
-
-# def move_images(source_folder, dest_folder):
-#     image_files = [f for f in os.listdir(source_folder) 
-#                    if f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))]
-#     os.makedirs(dest_folder, exist_ok=True)
-#     moved_count = 0
-#     for file in image_files:
-#         src_path = os.path.join(source_folder, file)
-#         if not is_empty(src_path):
-#             shutil.move(src_path, os.path.join(dest_folder, file))
-#             moved_count += 1
-#     return moved_count
-
-# def remove_low_size_images(data_path):
-#     images_list = os.listdir(data_path)
-#     low_size_photo_list = []
-#     for one_image in images_list:
-#         image_path = os.path.join(data_path, one_image)
-#         try:
-#             pic = imageio.imread(image_path)
-#             size = pic.size
-#             if size < 100:
-#                 low_size_photo_list.append(one_image)
-#         except:
-#             pass
-#     for one_image in low_size_photo_list[1:]:
-#         os.remove(os.path.join(data_path, one_image))
-
-# def calc_diff(img1 , img2) :
-#     i1 = Image.open(img1)
-#     i2 = Image.open(img2)
-#     h1 = imagehash.phash(i1)
-#     h2 = imagehash.phash(i2)
-#     return h1 - h2
-
-# def remove_duplicate_images(data_path) :
-#     image_files = os.listdir(data_path)
-#     only_images = []
-#     for one_image in image_files : 
-#         if one_image.endswith('jpeg') or one_image.endswith('png') or one_image.endswith('jpg') :
-#             only_images.append(one_image)
-#     only_images1 = sorted(only_images) 
-#     for one_image in only_images1 :
-#         for another_image in only_images1 :
-#             try :
-#                 if one_image == another_image :
-#                     continue
-#                 else :
-#                     diff = calc_diff(os.path.join(data_path ,one_image) , os.path.join(data_path ,another_image))
-#                     if diff ==0  :
-#                         os.remove(os.path.join(data_path , another_image))
-#             except Exception as e:
-#                 print(e)
-#                 pass
-# # from langchain_chroma import Chroma
-# # import chromadb
-# def initialize_qdrant(temp_dir , file_name , user):
-#     client = qdrant_client.QdrantClient(path=f"qdrant_mm_db_pipeline_{user}_{file_name}")
-#     # client = qdrant_client.QdrantClient(url = "http://localhost:2452")    
-#     # client = qdrant_client.QdrantClient(url="4b0af7be-d5b3-47ac-b215-128ebd6aa495.europe-west3-0.gcp.cloud.qdrant.io:6333", api_key="CO1sNGLmC6R_Q45qSIUxBSX8sxwHud4MCm4as_GTI-vzQqdUs-bXqw",)
-#     # client = qdrant_client.AsyncQdrantClient(location = ":memory:")
-        
-#     if "vectordatabase" not in st.session_state or not st.session_state.vectordatabase:
-            
-#         # text_store = client.create_collection(f"text_collection_pipeline_{user}_{file_name}"  ) 
-#         # image_store = client.create_collection(f"image_collection_pipeline_{user}_{file_name}"  ) 
-
-
-#         text_store = QdrantVectorStore( client = client , collection_name=f"text_collection_pipeline_{user}_{file_name}" )
-#         image_store = QdrantVectorStore(client = client , collection_name=f"image_collection_pipeline_{user}_{file_name}")
-#         storage_context = StorageContext.from_defaults(vector_store=text_store, image_store=image_store)
-#         documents = SimpleDirectoryReader(os.path.join(temp_dir, f"my_own_data_{user}_{file_name}")).load_data()
-#         index = MultiModalVectorStoreIndex.from_documents(documents, storage_context=storage_context)
-
-#         st.session_state.vectordatabase = index
-#     else :
-#         index = st.session_state.vectordatabase
-#     retriever_engine = index.as_retriever(similarity_top_k=1, image_similarity_top_k=1)
-#     return retriever_engine
-
-# def plot_images(image_paths):
-#     images_shown = 0
-#     plt.figure(figsize=(16, 9))
-#     for img_path in image_paths:
-#         if os.path.isfile(img_path):
-#             image = Image.open(img_path)
-#             plt.subplot(2, 3, images_shown + 1)
-#             plt.imshow(image)
-#             plt.xticks([])
-#             plt.yticks([])
-#             images_shown += 1
-#             if images_shown >= 6:
-#                 break
-
-# def retrieve_and_query(query, retriever_engine):
-#     retrieval_results = retriever_engine.retrieve(query)
-    
-#     qa_tmpl_str = (
-#         "Context information is below.\n"
-#         "---------------------\n"
-#         "{context_str}\n"
-#         "---------------------\n"
-#         "Given the context information , "
-#         "answer the query in detail.\n"
-#         "Query: {query_str}\n"
-#         "Answer: "
-#     )
-#     qa_tmpl = PromptTemplate(qa_tmpl_str)
-
-#     llm = OpenAI(model="gpt-4o", temperature=0)
-#     response_synthesizer = get_response_synthesizer(response_mode="refine", text_qa_template=qa_tmpl, llm=llm)
-
-#     response = response_synthesizer.synthesize(query, nodes=retrieval_results)
-    
-#     retrieved_image_path_list = []
-#     for node in retrieval_results:
-#         if (node.metadata['file_type'] == 'image/jpeg') or (node.metadata['file_type'] == 'image/png'):
-#             if node.score > 0.25:
-#                 retrieved_image_path_list.append(node.metadata['file_path'])
-    
-#     return response, retrieved_image_path_list
-# #tmpnimvp35m , tmpnimvp35m , tmpydpissmv
-# def process_pdf(pdf_file):
-#     temp_dir = tempfile.TemporaryDirectory()
-#     unique_folder_name = temp_dir.name.split('/')[-1]
-#     temp_pdf_path = os.path.join(temp_dir.name, pdf_file.name)
-#     with open(temp_pdf_path, "wb") as f:
-#         f.write(pdf_file.getvalue())
-
-#     data_path = os.path.join(temp_dir.name, f"my_own_data_{unique_folder_name}_{os.path.splitext(pdf_file.name)[0]}")
-#     os.makedirs(data_path , exist_ok=True) 
-#     img_save_path = os.path.join(temp_dir.name, f"extracted_images_{unique_folder_name}_{os.path.splitext(pdf_file.name)[0]}")
-#     os.makedirs(img_save_path , exist_ok=True) 
-
-#     extracted_text = extract_text_from_pdf(temp_pdf_path)
-#     with open(os.path.join(data_path, "content.txt"), "w") as file:
-#         file.write(extracted_text)
-
-#     extract_images_from_pdf(temp_pdf_path, img_save_path)
-#     moved_count = move_images(img_save_path, data_path)
-#     remove_low_size_images(data_path)
-#     remove_duplicate_images(data_path)
-#     retriever_engine = initialize_qdrant(temp_dir.name , os.path.splitext(pdf_file.name)[0] , unique_folder_name)
-
-#     return temp_dir, retriever_engine
-
-# def main():
-#     st.title("PDF Vector Database Query Tool")
-#     st.markdown("This tool creates a vector database from a PDF and allows you to query it.")
-    
-#     if "retriever_engine" not in st.session_state:
-#         st.session_state.retriever_engine = None
-#     if "vectordatabase" not in st.session_state:
-#         st.session_state.vectordatabase = None
-
-#     uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
-#     if uploaded_file is None:
-#         st.info("Please upload a PDF file.")
-#     else:
-#         st.info(f"Uploaded PDF: {uploaded_file.name}")
-#         if st.button("Process PDF"):
-#             with st.spinner("Processing PDF..."):
-#                 temp_dir, st.session_state.retriever_engine = process_pdf(uploaded_file)
-        
-#                 st.success("PDF processed successfully!")
-
-#     if st.session_state.retriever_engine :
-#         query = st.text_input("Enter your question:")
-        
-        
-#         if st.button("Ask Question"):
-#             print("running")
-#             try:
-
-#                 with st.spinner("Retrieving information..."):
-#                     response, retrieved_image_path_list = retrieve_and_query(query, st.session_state.retriever_engine)
-#                     print(retrieved_image_path_list)
-#                 st.write("Retrieved Context:")
-#                 for node in response.source_nodes:
-#                     st.code(node.node.get_text())
-                
-#                 st.write("\nRetrieved Images:")
-#                 plot_images(retrieved_image_path_list)
-#                 st.pyplot()
-
-#                 st.write("\nFinal Answer:")
-#                 st.code(response.response)
-            
-#             except Exception as e:
-#                 st.error(f"An error occurred: {e}")
-
-# if __name__ == "__main__":
-#     main()
-
-
-
 import streamlit as st
-from PIL import Image 
 
 from pdf_processing import process_pdf
 from retrieve_and_display import retrieve_and_query, plot_images
@@ -281,13 +24,7 @@ def upload_file():
                 st.session_state.filename_and_retriever_engine = uploaded_file.name, process_pdf(uploaded_file)
                 st.success("PDF processed successfully!")
                 st.success("Click on Chat in sidebar")
-
-def img_display(img_path_list) :
-    ##################### new image display function ###################################
-    for one_img in img_path_list :
-        image = Image.open(one_img) 
-        st.image(image)
-   
+    
 
 def ask_question():
     if st.session_state.filename_and_retriever_engine :
@@ -295,16 +32,15 @@ def ask_question():
         if user_question := st.chat_input("Ask a question"):
             with st.spinner("Retrieving information..."):
                 response, retrieved_image_path_list = retrieve_and_query(user_question, st.session_state.filename_and_retriever_engine[1])
-                # st.session_state.filename_and_retriever_engine[1].count('image_collection')
                 print(retrieved_image_path_list)
             st.write("Retrieved Context:")
             for node in response.source_nodes:
                 st.code(node.node.get_text())
             
             st.write("\nRetrieved Images:")
-            # plot_images(retrieved_image_path_list)
-            img_display(retrieved_image_path_list)
-            # st.pyplot()
+            # if len(retrieved_image_path_list) > 0:
+            plot_images(retrieved_image_path_list)
+                # st.pyplot()
 
             st.write("\nFinal Answer:")
             st.code(response.response)
@@ -327,3 +63,4 @@ def main():
 if __name__ == "__main__":
     # login_page()
     main()
+

awsfunctions.py

@@ -14,7 +14,7 @@ def upload_folder_to_s3(local_dir, prefix=''):
             
             # Create the directory in S3 if it doesn't exist
             try:
-                s3_client.put_object(Bucket=s3_bucket, Key=f"{prefix}{relative_path}/")
+                s3_client.put_object(Bucket=s3_bucket, Key=os.path.join(prefix, relative_path))
             except ClientError as e:
                 if e.response['Error']['Code'] == '404':
                     continue  # Directory already exists
@@ -26,8 +26,8 @@ def upload_folder_to_s3(local_dir, prefix=''):
             relative_path = os.path.relpath(file_path, local_dir)
             
             try:
-                s3_client.upload_file(file_path, s3_bucket, f"{prefix}{relative_path}")
-                print(f"Uploaded: {file_path} -> s3://{s3_bucket}/{prefix}{relative_path}")
+                s3_client.upload_file(file_path, s3_bucket, os.path.join(prefix, relative_path))
+                print(f"Uploaded: {file_path} -> s3://{s3_bucket}/{os.path.join(prefix, relative_path)}")
             except Exception as e:
                 raise e
                 # print(f"Error uploading {file_path}: {e}")

retrieve_and_display.py

@@ -22,13 +22,14 @@ def plot_images(image_paths):
     for img_path in image_paths:
         if check_file_exists_in_s3(img_path):
             image = get_image_from_s3(img_path)
-            plt.subplot(2, 3, images_shown + 1)
-            plt.imshow(image)
-            plt.xticks([])
-            plt.yticks([])
-            images_shown += 1
-            if images_shown >= 6:
-                break
+            st.image(image)
+            # plt.subplot(2, 3, images_shown + 1)
+            # plt.imshow(image)
+            # plt.xticks([])
+            # plt.yticks([])
+            # images_shown += 1
+            # if images_shown >= 6:
+            #     break
 
 def retrieve_and_query(query, retriever_engine):
     retrieval_results = retriever_engine.retrieve(query)
@@ -45,7 +46,7 @@ def retrieve_and_query(query, retriever_engine):
     )
     qa_tmpl = PromptTemplate(qa_tmpl_str)
 
-    llm = OpenAI(model="gpt-4o", temperature=0)
+    llm = OpenAI(model="gpt-4o-mini", temperature=0)
     response_synthesizer = get_response_synthesizer(response_mode="refine", text_qa_template=qa_tmpl, llm=llm)
 
     response = response_synthesizer.synthesize(query, nodes=retrieval_results)