Update app.py

app.py

@@ -10,149 +10,142 @@ import os
 import google.generativeai as genai
 from dotenv import load_dotenv
 
-# Load environment variables
-load_dotenv()
+load_dotenv() # Load all env variables 
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
 
-def process_pdfs(pdf_files):
-  """
-  Extracts text from uploaded PDFs and splits them into chunks.
-  """
-  text = ""
-  for pdf in pdf_files:
-    reader = PdfReader(pdf)
-    for page in reader.pages:
-      text += page.extract_text()
-  return get_text_chunks(text)
+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
 
 
 def get_text_chunks(text):
-  """
-  Splits the text into manageable chunks for processing.
-  """
-  splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-  return splitter.split_text(text)
-
-
-def create_vector_store(text_chunks):
-  """
-  Creates a vector store for efficient text retrieval.
-  """
-  embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-  return FAISS.from_texts(text_chunks, embedding=embeddings).save_local("faiss_index")
-
-
-def build_qa_chain():
-  """
-  Defines the prompt template and loads the question-answering chain.
-  """
-  prompt_template = """
-  Answer the question in detail, considering the provided context. 
-  If the answer is not available, state "answer unavailable".\n\n
-  Context: {context}?\n
-  Question: {question}\n
-
-  Answer:
-  """
-  model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
-  prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
-  return load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
-
-def answer_question(question, embeddings, vector_store):
-  """
-  Searches the vector store and retrieves relevant documents for answering.
-  """
-  docs = vector_store.similarity_search(question)
-  qa_chain = get_conversational_chain()
-  response = qa_chain({"input_documents": docs, "question": question}, return_only_outputs=True)
-  return response["output_text"]
-
-
-def get_gemini_response(input_text, image=None):
-  """
-  Generates a response using the Gemini Pro model, potentially with an image.
-  """
-  model = genai.GenerativeModel("gemini-pro-vision")
-  if image is not None and model.is_image_model:
-    response = model.generate_content([input_text, image])
-  else:
-    response = model.generate_content(input_text)
-  return response.text
-
-
-def pdf_chat_app():
-  """
-  Handles the PDF chat functionality.
-  """
-  st.header("PDF Chat Application")
-  user_question = st.text_input("Ask a Question from the PDF Files")
-  uploaded_files = st.file_uploader("Upload PDF Files", accept_multiple_files=True)
-
-  if uploaded_files:
-    with st.spinner("Processing..."):
-      text_chunks = process_pdfs(uploaded_files)
-      create_vector_store(text_chunks)
-      embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-      vector_store = FAISS.load_local("faiss_index", embeddings)
-      st.success("Done!")
-
-  if user_question and uploaded_files:
-    answer = answer_question(user_question, embeddings, vector_store)
-    st.write("Reply:", answer)
-
-def image_chat_app():
-  """
-  Handles the image chat functionality.
-  """
-  st.header("Image Chat Application")
-  input_text = st.text_input("Input for Gemini Pro:")
-  uploaded_image = st.file_uploader("Choose an image...", type="jpg")
-  if uploaded_image is not None:
-    image = Image.open(uploaded_image)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-
-  submit_gemini = st.button("Ask Gemini Pro")
-
-  if submit_gemini:
-    response_gemini = get_gemini_response(input_text, image)
-    st.subheader("Gemini Pro Response:")
-    st.write(response_gemini)
-
-
-def qa_chat_app():
-  """
-  Handles the Q&A chat functionality.
-  """
-  st.header("Q&A Chat Application")
-  # Initialize session state for chat history if it doesn't exist
-  if 'chat_history' not in st.session_state:
-    st.session_state['chat_history'] = []
-
-  input_qa = st.text_area("Input for Q&A:", key="input_qa")
-  submit_qa = st.button("Ask the question")
-
-  if submit_qa and input_qa:
-    response_qa = get_gemini_response(input_qa)
-    # Add user query and response to session state chat history
-    st.session_state['chat_history'].append(("You", input_qa))
-    st.subheader("Q&A Response:")
-    for chunk in response_qa:
-      st.write(chunk.text)
-    st.session_state['chat_history'].append(("Gemini Pro", chunk.text))
-
-  st.subheader("Q&A Chat History:")
-  for role, text in st.session_state['chat_history']:
-    st.write(f"{role}: {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")
+    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
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+    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)
+    docs = new_db.similarity_search(user_question)
+    chain = get_conversational_chain()
+    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+    st.write("Reply: ", response["output_text"])
+
+
+## function to load Gemini Pro model and get responses
+model = genai.GenerativeModel("gemini-pro-vision")
+def get_gemini_response(input, image=None):
+    if image is not None and model.is_image_model:
+        response = model.generate_content([input, image])
+    else:
+        response = model.generate_content(input)
+    return response.text
+
+
+## Initialize our Streamlit app
+
+st.set_page_config(page_title='Combined Streamlit Application')
+st.header("Streamlit Application")
+
+# Define the different applications
+applications = {
+    "PDF Chat": "pdf_chat",
+    "Image Chat": "image_chat",
+    "Q&A Chat": "qa_chat"
+}
+
+# Render the dropdown menu
+selected_app = st.sidebar.selectbox("Select Application", list(applications.keys()))
+
+# Function to display PDF Chat application
+def pdf_chat():
+    st.header("PDF Chat Application")
+    user_question = st.text_input("Ask a Question from the PDF Files")
+    if user_question:
+        user_input(user_question)
+
+    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")
+
+# Function to display Image Chat application
+def image_chat():
+    st.header("Image Chat Application")
+    input_text = st.text_input("Input for Gemini Pro:", key="input_gemini")
+    uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    submit_gemini = st.button("Ask Gemini Pro")
+
+    if submit_gemini:
+        response_gemini = get_gemini_response(input_text, image)
+        st.subheader("Gemini Pro Response:")
+        st.write(response_gemini)
+
+# Function to display Q&A Chat application
+def qa_chat():
+    st.header("Q&A Chat Application")
+    # Initialize session state for chat history if it doesn't exist
+    if 'chat_history' not in st.session_state:
+        st.session_state['chat_history'] = []
+
+    input_qa = st.text_area("Input for Q&A:", key="input_qa")
+    submit_qa = st.button("Ask the question")
+
+    if submit_qa and input_qa:
+        response_qa = get_gemini_response(input_qa)
+        # Add user query and response to session state chat history
+        st.session_state['chat_history'].append(("You", input_qa))
+        st.subheader("Q&A Response:")
+        for chunk in response_qa:
+            st.write(chunk.text)
+            st.session_state['chat_history'].append(("Gemini Pro", chunk.text))
+
+    st.subheader("Q&A Chat History:")
+    for role, text in st.session_state['chat_history']:
+        st.write(f"{role}: {text}")
+
 # Map selected application to corresponding function
 selected_app_func = {
-  "PDF Chat": pdf_chat,
-  "Image Chat": image_chat,
-  "Q&A Chat": qa_chat
+    "PDF Chat": pdf_chat,
+    "Image Chat": image_chat,
+    "Q&A Chat": qa_chat
 }
 
 # Run the selected application function
-selected_app_func[selected_app]()
+selected_app_func[selected_app]()