import streamlit as st
import pandas as pd
import google.generativeai as genai

GOOGLE_API_KEY_VALUE="AIzaSyCyHMkaiwpZjzfuSwQnFfaD6l6RXTmWJkY"
genai.configure(api_key=GOOGLE_API_KEY_VALUE)
model = genai.GenerativeModel('gemini-pro')

def process_csv(uploaded_file):
    if uploaded_file is not None:
        data = pd.read_csv(uploaded_file,header=0)
        st.write("File loaded")
        values = data[data.columns[0]].tolist()

        st.write("No of Data entities to be processed:", len(values))
        #st.markdown("* " + "\n* ".join([f"data is {value}" for value in values]))

        return values

def call_gemini_model(values):
    responses = []
    for value in values:
        prompt = f"Give 5 line details on {value}"
        response = model.generate_content(prompt)
        st.write(f"Response for {value}:")
        st.write(response.text)
        responses.append(response.text)

    return responses

def main():
    st.title("CSV Processor and Gemini Model")

    uploaded_file = st.file_uploader("Upload a CSV file with only 1 column containing the names of the entity, The 1st row shoud be the header i.e. column name")

    if uploaded_file is not None:
        values = process_csv(uploaded_file)
        if values:
            responses = call_gemini_model(values)
            x=len(responses)
            #st.write(x)
            #create_pdf(responses)

            st.write("Total responses processed: ",len(responses))
            #for response in responses:
             #   st.markdown(f"* {response}")

            #with open("gemini_responses.pdf", "rb") as pdf_file:
            #    st.download_button("Download PDF", pdf_file, file_name="gemini_responses.pdf")



if __name__ == "__main__":
    main()