app.py

import streamlit as st
from google.cloud import aiplatform
import pdfplumber
import google.generativeai as genai
import os
from PIL import Image

# Set the environment variable
genai.configure(api_key="API_KEY") #use Secrets when deploy on GC Run
# Function to extract text from PDF using pdfplumber
def extract_text_from_pdf(pdf_file):
    text = ""
    with pdfplumber.open(pdf_file) as pdf:
        for page in pdf.pages:
            text += page.extract_text() or ""
    return text

# Function to get a summary from Gemini-pro API
def summarize_text(text):
    try:
        model = genai.GenerativeModel("gemini-1.5-flash")
        response = model.generate_content([f"Please summarize the following text:\n\n{text}"])  # Increase timeout
        return response.text
    except Exception as e:
        return f"An error occurred: {e}"

# Function to question PDF using Gemini-pro API
def question_text(text, question):
    try:
        model = genai.GenerativeModel("gemini-1.5-flash")
        response = model.generate_content([f"You are a travel agency and you give suggestions only concerning travels. Answer the following question \
        based on the provided text:\n\nText: {text}\n\nQuestion: {question}"])  # Increase timeout
        return response.text
    except Exception as e:
        return f"An error occurred: {e}"


# Function to question text using Gemini-pro API
def generic_question(question):
    try:
        model = genai.GenerativeModel("gemini-1.5-flash")
        response = model.generate_content([f"You are a travel agency and you give suggestions only concerning travels. Answer the following question: {question}"])  # Increase timeout
        return response.text
    except Exception as e:
        return f"An error occurred: {e}"

# Streamlit app
def main():

    model = genai.GenerativeModel("gemini-1.5-flash")
    st.title("Travel Agency with Gemini")
    
    # Ask a generic question
    st.header("What are your travel needs?")

    # Gemini uses 'model' for assistant; Streamlit uses 'assistant'
    def role_to_streamlit(role):
      if role == "model":
        return "assistant"
      else:
        return role

    # Add a Gemini Chat history object to Streamlit session state
    if "chat" not in st.session_state:
        st.session_state.chat = model.start_chat(history = [])

    # Create a container for chat messages
    chat_container = st.container()

    # Display chat messages from history above current input box
    with chat_container:
      for message in st.session_state.chat.history:
          with st.chat_message(role_to_streamlit(message.role)):
              st.markdown(message.parts[0].text)
      message_placeholder = st.empty()

    # Accept user's next message, add to context, resubmit context to Gemini
    if prompt := st.chat_input("What would you like to know?"):
        # Display user's last message
        with chat_container:
          st.chat_message("user").markdown(prompt)

        # Send user entry to Gemini and read the response
        response = st.session_state.chat.send_message(prompt)

        # Display last
        with chat_container:
          with st.chat_message("assistant"):
              st.markdown(response.text)

    with st.sidebar:
    # Ask about a PDF content
      st.header("We explain you the PDF you show us!")

      uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")


      if uploaded_file is not None:
          # Extract text from the uploaded PDF
          text = extract_text_from_pdf(uploaded_file)

          # Limit the text displayed to 500 characters
          display_text = text[:500] + ('...' if len(text) > 500 else '')

          # Display the extracted text
          st.subheader("Extracted Text")
          st.text_area("Text from PDF", display_text, height=300)

          # Get a summary
          if st.button("Get Summary"):
              summary = summarize_text(text)
              st.subheader("Summary")
              st.write(summary)

          # Ask a question
          question = st.text_input("Enter your question about the text")
          if st.button("Get Answer"):
              if question:
                  answer = question_text(text, question)
                  st.subheader("Answer")
                  st.write(answer)
              else:
                  st.warning("I am an AI, I read data not minds. Please enter a question to get an answer.")

if __name__ == "__main__":
    main()