Update app.py

app.py

@@ -1,12 +1,19 @@
-import streamlit as st
+import streamlit as st 
 import os
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import base64
+import time
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM 
 from transformers import pipeline
-import torch
-from langchain.embeddings import SentenceTransformerEmbeddings
-from langchain.vectorstores import Chroma
+import torch 
+import textwrap 
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader 
+from langchain.text_splitter import RecursiveCharacterTextSplitter 
+from langchain.embeddings import SentenceTransformerEmbeddings 
+from langchain.vectorstores import Chroma 
 from langchain.llms import HuggingFacePipeline
-from langchain.chains import RetrievalQA
+from langchain.chains import RetrievalQA 
+from constants import CHROMA_SETTINGS
+from streamlit_chat import message
 
 st.set_page_config(layout="wide")
 
@@ -21,28 +28,45 @@ base_model = AutoModelForSeq2SeqLM.from_pretrained(
     torch_dtype=torch.float32
 )
 
-persist_directory = "db"
 
-@st.cache_resource
-def data_ingestion(pdf_content):
-    # Process the PDF content here and generate a brief summary.
-    # You can use libraries like PyPDF2, pdfminer, or other PDF processing tools.
+# checkpoint = "LaMini-T5-738M"
+# tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# base_model = AutoModelForSeq2SeqLM.from_pretrained(
+#     checkpoint,
+#     device_map="auto",
+#     torch_dtype = torch.float32,
+#     from_tf=True
+# )
 
-    # For now, let's assume we have extracted the text from the PDF.
-    pdf_text = "This is a brief summary of the PDF content."
+persist_directory = "db"
 
-    return pdf_text
+@st.cache_resource
+def data_ingestion():
+    for root, dirs, files in os.walk("docs"):
+        for file in files:
+            if file.endswith(".pdf"):
+                print(file)
+                loader = PDFMinerLoader(os.path.join(root, file))
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500)
+    texts = text_splitter.split_documents(documents)
+    #create embeddings here
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    #create vector store here
+    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+    db.persist()
+    db=None 
 
 @st.cache_resource
 def llm_pipeline():
     pipe = pipeline(
         'text2text-generation',
-        model=base_model,
-        tokenizer=tokenizer,
-        max_length=256,
-        do_sample=True,
-        temperature=0.3,
-        top_p=0.95,
+        model = base_model,
+        tokenizer = tokenizer,
+        max_length = 256,
+        do_sample = True,
+        temperature = 0.3,
+        top_p= 0.95,
         device=device
     )
     local_llm = HuggingFacePipeline(pipeline=pipe)
@@ -52,12 +76,12 @@ def llm_pipeline():
 def qa_llm():
     llm = llm_pipeline()
     embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
-    db = Chroma(persist_directory="db", embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+    db = Chroma(persist_directory="db", embedding_function = embeddings, client_settings=CHROMA_SETTINGS)
     retriever = db.as_retriever()
     qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=retriever,
+        llm = llm,
+        chain_type = "stuff",
+        retriever = retriever,
         return_source_documents=True
     )
     return qa
@@ -70,29 +94,87 @@ def process_answer(instruction):
     answer = generated_text['result']
     return answer
 
+def get_file_size(file):
+    file.seek(0, os.SEEK_END)
+    file_size = file.tell()
+    file.seek(0)
+    return file_size
+
+@st.cache_data
+#function to display the PDF of a given file 
+def displayPDF(file):
+    # Opening file from file path
+    with open(file, "rb") as f:
+        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
+
+    # Embedding PDF in HTML
+    pdf_display = F'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
+
+    # Displaying File
+    st.markdown(pdf_display, unsafe_allow_html=True)
+
+# Display conversation history using Streamlit messages
+def display_conversation(history):
+    for i in range(len(history["generated"])):
+        message(history["past"][i], is_user=True, key=str(i) + "_user")
+        message(history["generated"][i],key=str(i))
+
 def main():
     st.markdown("<h1 style='text-align: center; color: blue;'>Chat with your PDF 🦜📄 </h1>", unsafe_allow_html=True)
-    st.markdown("<h3 style='text-align: center; color: grey;'>Built by <a href='https://github.com/manas95826'>The Manas with ❤️ </a></h3>", unsafe_allow_html=True)
+    st.markdown("<h3 style='text-align: center; color: grey;'>Built by <a href='https://github.com/AIAnytime'>AI Anytime with ❤️ </a></h3>", unsafe_allow_html=True)
 
-    st.markdown("<h2 style='text-align: center; color:red;'>Ask Questions about your PDF 👇</h2>", unsafe_allow_html=True)
+    st.markdown("<h2 style='text-align: center; color:red;'>Upload your PDF 👇</h2>", unsafe_allow_html=True)
 
-    user_input = st.text_input("Ask a question:", key="input")
+    uploaded_file = st.file_uploader("", type=["pdf"])
+
+    if uploaded_file is not None:
+        file_details = {
+            "Filename": uploaded_file.name,
+            "File size": get_file_size(uploaded_file)
+        }
+        filepath = "docs/"+uploaded_file.name
+        with open(filepath, "wb") as temp_file:
+                temp_file.write(uploaded_file.read())
+
+        col1, col2= st.columns([1,2])
+        with col1:
+            st.markdown("<h4 style color:black;'>File details</h4>", unsafe_allow_html=True)
+            st.json(file_details)
+            st.markdown("<h4 style color:black;'>File preview</h4>", unsafe_allow_html=True)
+            pdf_view = displayPDF(filepath)
+
+        with col2:
+            with st.spinner('Embeddings are in process...'):
+                ingested_data = data_ingestion()
+            st.success('Embeddings are created successfully!')
+            st.markdown("<h4 style color:black;'>Chat Here</h4>", unsafe_allow_html=True)
+
+
+            user_input = st.text_input("", key="input")
+
+            # Initialize session state for generated responses and past messages
+            if "generated" not in st.session_state:
+                st.session_state["generated"] = ["I am ready to help you"]
+            if "past" not in st.session_state:
+                st.session_state["past"] = ["Hey there!"]
+                
+            # Search the database for a response based on user input and update session state
+            if user_input:
+                answer = process_answer({'query': user_input})
+                st.session_state["past"].append(user_input)
+                response = answer
+                st.session_state["generated"].append(response)
+
+            # Display conversation history using Streamlit messages
+            if st.session_state["generated"]:
+                display_conversation(st.session_state)
+        
+
+        
 
-    # Initialize session state for generated responses
-    if "generated" not in st.session_state:
-        st.session_state["generated"] = []
 
-    # Search the database for a response based on user input and update session state
-    if user_input:
-        answer = process_answer({'query': user_input})
-        st.session_state["generated"].append(answer)
 
-    # Display conversation history using Streamlit messages
-    if st.session_state["generated"]:
-        st.markdown("<h4>Conversation History:</h4>", unsafe_allow_html=True)
-        for i, response in enumerate(st.session_state["generated"]):
-            st.text(f"Q{i+1}: {user_input}")
-            st.text(f"A{i+1}: {response}")
 
 if __name__ == "__main__":
     main()
+