Update app.py

app.py

@@ -14,87 +14,67 @@ genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
 
 def get_pdf_text(pdf_docs):
-    text=""
+    text = ""
     for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
+        pdf_reader = PdfReader(pdf)
         for page in pdf_reader.pages:
-            text+= page.extract_text()
-    return  text
-
-
+            text += page.extract_text()
+    return text
 
 def get_text_chunks(text):
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
     chunks = text_splitter.split_text(text)
     return chunks
 
-
 def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
     vector_store.save_local("faiss_index")
 
-
 def get_conversational_chain():
-
     prompt_template = """
     Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
-    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+    provided context just say, "answer is not available in the Provided PDF", don't provide the wrong answer\n\n
     Context:\n {context}?\n
     Question: \n{question}\n
 
     Answer:
     """
-
-    model = ChatGoogleGenerativeAI(model="gemini-pro",
-                             temperature=0.1)
-
-    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.1)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
     return chain
 
-
-
 def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    
-    new_db = FAISS.load_local("faiss_index", embeddings,allow_dangerous_deserialization= True)
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
     docs = new_db.similarity_search(user_question)
-
     chain = get_conversational_chain()
-
-    
-    response = chain(
-        {"input_documents":docs, "question": user_question}
-        , return_only_outputs=True)
-
-    print(response)
-    st.write("Reply: ", response["output_text"])
-
-
-
-
-def main():
-    st.set_page_config("Chat PDF")
-    st.header("QnA with Multiple PDF files💁")
-
-    user_question = st.text_input("Ask a Question from the PDF Files")
-
-    if user_question:
-        user_input(user_question)
-
-    with st.sidebar:
-        st.title("Menu:")
-        pdf_docs = 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..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("Done")
-
-
-
-if __name__ == "__main__":
-    main()
+    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+    return response["output_text"]
+
+# Streamlit app initialization
+st.title("Chat With PDF 📄")
+
+if 'messages' not in st.session_state:
+    st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]
+
+with st.sidebar:
+    st.title("Menu:")
+    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..."):
+            raw_text = get_pdf_text(uploaded_file)
+            text_chunks = get_text_chunks(raw_text)
+            get_vector_store(text_chunks)
+            st.success("Done")
+
+user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
+if user_prompt:
+    st.session_state.messages.append({'role': 'user', "content": user_prompt})
+    response = user_input(user_prompt)
+    st.session_state.messages.append({'role': 'assistant', "content": response})
+
+for message in st.session_state.messages:
+    with st.chat_message(message['role']):
+        st.write(message['content'])