Update app.py

app.py

@@ -1,9 +1,10 @@
-import streamlit as st
 import os
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain import LLMChain
 from streamlit_chat import message
 import google.generativeai as genai
 from langchain.prompts import PromptTemplate
-from langchain import LLMChain
 from langchain_google_genai import ChatGoogleGenerativeAI
 
 os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
@@ -14,13 +15,15 @@ llm = ChatGoogleGenerativeAI(model="gemini-pro",
 
 
 template = """You are a friendly chat assistant called "CRETA" having a conversation with a human and you are created by Pachaiappan an AI Specialist.
+provided document:
+{provided_docs}
 previous_chat:
 {chat_history}
 Human: {human_input}
 Chatbot:"""
 
 prompt = PromptTemplate(
-    input_variables=["chat_history", "human_input"], template=template
+    input_variables=["chat_history", "human_input", "provided_docs"], template=template
 )
 
 llm_chain = LLMChain(
@@ -31,13 +34,16 @@ llm_chain = LLMChain(
 
 
 previous_response = ""
+provided_docs = ""
 def conversational_chat(query):
-    global previous_response
+    global previous_response, provided_docs
     for i in st.session_state['history']:
         if i is not None:
             previous_response += f"Human: {i[0]}\n Chatbot: {i[1]}"
-    print(previous_response)
-    result = llm_chain.predict(chat_history=previous_response, human_input=query)
+    for j in st.session_state["docs"]:
+        if j is not None:
+            provided_docs += j
+    result = llm_chain.predict(chat_history=previous_response, human_input=query, provided_docs=provided_docs)
     st.session_state['history'].append((query, result))
     return result
 
@@ -54,6 +60,25 @@ if 'generated' not in st.session_state:
 if 'past' not in st.session_state:
     st.session_state['past'] = [" "]
     
+if 'docs' not in st.session_state:
+    st.session_state['docs'] = []
+    
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+with st.sidebar:
+    st.title("Add a file for CRETA memory:")
+    uploaded_file = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+    if st.button("Submit & Process"):
+        with st.spinner("Processing..."):
+            st.session_state["docs"] += get_pdf_text(uploaded_file)
+            st.success("Done")
+    
 # Create containers for chat history and user input
 response_container = st.container()
 container = st.container()
@@ -66,11 +91,12 @@ with container:
         # answer = response_generator(output)
         st.session_state['past'].append(user_input)
         st.session_state['generated'].append(output)
-
+        
+        
 # Display chat history
 if st.session_state['generated']:
     with response_container:
         for i in range(len(st.session_state['generated'])):
             if i != 0:
                 message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="adventurer")
-            message(st.session_state["generated"][i], key=str(i), avatar_style="bottts")
+            message(st.session_state["generated"][i], key=str(i), avatar_style="bottts")