Update app.py

app.py

@@ -1,20 +1,124 @@
-import os
+# import os
+
+# import streamlit as st
+# from dotenv import load_dotenv
+# from PyPDF2 import PdfReader
+# from langchain.text_splitter import CharacterTextSplitter
+# from langchain_openai import OpenAIEmbeddings
+# from langchain.vectorstores import FAISS
+# # from langchain_community.vectorstores import FAISS
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.memory import ConversationBufferMemory
+# from langchain.chains import ConversationalRetrievalChain
+# from langchain.chat_models import ChatOpenAI
+# from htmlTemplates import css, bot_template, user_template
+# from langchain.embeddings import HuggingFaceInstructEmbeddings
+# from langchain.llms import HuggingFaceHub
+# import os
+# def get_pdf_text(pdf_doc):
+#     text = ""
+#     for pdf in pdf_doc:
+#         pdf_reader = PdfReader(pdf)
+#         for page in pdf_reader.pages:
+#             text += page.extract_text()
+#     return text
+
+
+# def get_text_chunk(row_text):
+#     text_splitter = CharacterTextSplitter(
+#         separator="\n",
+#         chunk_size = 1000,
+#         chunk_overlap = 200,
+#         length_function = len
+#     )
+#     chunk = text_splitter.split_text(row_text)
+#     return chunk
+
+
+# def get_vectorstore(text_chunk):
+#     #embeddings = OpenAIEmbeddings(openai_api_key = os.getenv("OPENAI_API_KEY"))
+#     embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+#     vector = FAISS.from_texts(text_chunk,embeddings)
+#     return vector
+
+
+# def get_conversation_chain(vectorstores):
+#     #llm = ChatOpenAI(openai_api_key = os.getenv("OPENAI_API_KEY"))
+#     llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature":0.5, "max_length":512})
+#     memory = ConversationBufferMemory(memory_key = "chat_history",return_messages = True)
+#     conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm,
+#                                                                retriever=vectorstores.as_retriever(),
+#                                                                memory=memory)
+#     return conversation_chain
+
+
+# def user_input(user_question):
+#     response = st.session_state.conversation({"question":user_question})
+#     st.session_state.chat_history = response["chat_history"]
+
+#     for indx, msg in enumerate(st.session_state.chat_history):
+#         if indx % 2==0:
+#             st.write(user_template.replace("{{MSG}}",msg.content), unsafe_allow_html=True)
+#         else:
+#             st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
+
+
+
+# def main():
+#     # load secret key
+#     load_dotenv()
+    
+#     # config the pg
+#     st.set_page_config(page_title="Chat with multiple PDFs" ,page_icon=":books:")
+#     st.write(css, unsafe_allow_html=True)
+#     if "conversation" not in st.session_state:
+#         st.session_state.conversation = None
+
+#     st.header("Chat with multiple PDFs :books:")
+#     user_question = st.text_input("Ask a question about your docs")
+#     if user_question:
+#         user_input(user_question)
+
+#     # st.write(user_template.replace("{{MSG}}","Hello Robot"), unsafe_allow_html=True)
+#     # st.write(bot_template.replace("{{MSG}}","Hello Human"), unsafe_allow_html=True)
+
+#     # create side bar
+#     with st.sidebar:
+#         st.subheader("Your Documents")
+#         pdf_doc = st.file_uploader(label="Upload your documents",accept_multiple_files=True)
+#         if st.button("Process"):
+#             with st.spinner(text="Processing"):
+
+#             # get pdf text
+#                 row_text = get_pdf_text(pdf_doc)
+#             # get the text chunk
+#                 text_chunk = get_text_chunk(row_text)
+#                 # st.write(text_chunk)
+#             # create vecor store
+#                 vectorstores = get_vectorstore(text_chunk)
+#                 # st.write(vectorstores)
+#             # create conversation chain
+#                 st.session_state.conversation = get_conversation_chain(vectorstores)
+
+
+# if __name__ == "__main__":
+#     main()
 
+import os
 import streamlit as st
 from dotenv import load_dotenv
 from PyPDF2 import PdfReader
+from pdf2image import convert_from_path
 from langchain.text_splitter import CharacterTextSplitter
-from langchain_openai import OpenAIEmbeddings
+from sentence_transformers import SentenceTransformer
 from langchain.vectorstores import FAISS
-# from langchain_community.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
-from langchain.chat_models import ChatOpenAI
-from htmlTemplates import css, bot_template, user_template
-from langchain.embeddings import HuggingFaceInstructEmbeddings
 from langchain.llms import HuggingFaceHub
-import os
+from htmlTemplates import css, bot_template, user_template
+from transformers import pipeline
+
+# Function to extract text from PDF
 def get_pdf_text(pdf_doc):
     text = ""
     for pdf in pdf_doc:
@@ -23,54 +127,69 @@ def get_pdf_text(pdf_doc):
             text += page.extract_text()
     return text
 
+# Function to extract images from PDF
+def get_pdf_images(pdf_doc):
+    images = []
+    for pdf in pdf_doc:
+        images.extend(convert_from_path(pdf))
+    return images
 
+# Function to split text into chunks
 def get_text_chunk(row_text):
     text_splitter = CharacterTextSplitter(
         separator="\n",
-        chunk_size = 1000,
-        chunk_overlap = 200,
-        length_function = len
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
     )
     chunk = text_splitter.split_text(row_text)
     return chunk
 
-
+# Function to create vector store
 def get_vectorstore(text_chunk):
-    #embeddings = OpenAIEmbeddings(openai_api_key = os.getenv("OPENAI_API_KEY"))
-    embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-    vector = FAISS.from_texts(text_chunk,embeddings)
+    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+    embeddings = model.encode(text_chunk)
+    vector = FAISS.from_embeddings(embeddings)
     return vector
 
-
+# Function to create conversation chain
 def get_conversation_chain(vectorstores):
-    #llm = ChatOpenAI(openai_api_key = os.getenv("OPENAI_API_KEY"))
-    llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature":0.5, "max_length":512})
-    memory = ConversationBufferMemory(memory_key = "chat_history",return_messages = True)
-    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm,
-                                                               retriever=vectorstores.as_retriever(),
-                                                               memory=memory)
+    llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature": 0.5, "max_length": 512})
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vectorstores.as_retriever(),
+        memory=memory
+    )
     return conversation_chain
 
-
+# Function to handle user input
 def user_input(user_question):
-    response = st.session_state.conversation({"question":user_question})
+    response = st.session_state.conversation({"question": user_question})
     st.session_state.chat_history = response["chat_history"]
 
     for indx, msg in enumerate(st.session_state.chat_history):
-        if indx % 2==0:
-            st.write(user_template.replace("{{MSG}}",msg.content), unsafe_allow_html=True)
+        if indx % 2 == 0:
+            st.write(user_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
         else:
             st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
 
+# Function to generate images from text using a DALL-E model
+def generate_image_from_text(prompt):
+    # Ensure you have a DALL-E or similar model for text-to-image generation
+    generator = pipeline("text-to-image", model="dalle-mini/dalle-mini")
+    images = generator(prompt)
+    return images
 
-
+# Main function
 def main():
-    # load secret key
+    # Load secret key
     load_dotenv()
-    
-    # config the pg
-    st.set_page_config(page_title="Chat with multiple PDFs" ,page_icon=":books:")
+
+    # Config the page
+    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
     st.write(css, unsafe_allow_html=True)
+
     if "conversation" not in st.session_state:
         st.session_state.conversation = None
 
@@ -79,27 +198,31 @@ def main():
     if user_question:
         user_input(user_question)
 
-    # st.write(user_template.replace("{{MSG}}","Hello Robot"), unsafe_allow_html=True)
-    # st.write(bot_template.replace("{{MSG}}","Hello Human"), unsafe_allow_html=True)
-
-    # create side bar
+    # Create side bar
     with st.sidebar:
         st.subheader("Your Documents")
-        pdf_doc = st.file_uploader(label="Upload your documents",accept_multiple_files=True)
+        pdf_doc = st.file_uploader(label="Upload your documents", accept_multiple_files=True, type=["pdf"])
         if st.button("Process"):
             with st.spinner(text="Processing"):
-
-            # get pdf text
+                # Get PDF text
                 row_text = get_pdf_text(pdf_doc)
-            # get the text chunk
+                # Get the text chunk
                 text_chunk = get_text_chunk(row_text)
-                # st.write(text_chunk)
-            # create vecor store
+                # Create vector store
                 vectorstores = get_vectorstore(text_chunk)
-                # st.write(vectorstores)
-            # create conversation chain
+                # Create conversation chain
                 st.session_state.conversation = get_conversation_chain(vectorstores)
 
+                # Extract and display images from PDFs
+                images = get_pdf_images(pdf_doc)
+                for img in images:
+                    st.image(img)
+
+                # Generate and display images from text using DALL-E
+                if user_question:
+                    generated_images = generate_image_from_text(user_question)
+                    for gen_img in generated_images:
+                        st.image(gen_img)
 
 if __name__ == "__main__":
-    main()
+    main()