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import os
import google.generativeai as genai
import streamlit as st
from PyPDF2 import PdfReader
from collections import Counter
import re
# Get the API key from environment variable
api_key = os.getenv("GEMINI_API_KEY")
if api_key is None:
st.error("API key not found. Please set the GEMINI_API_KEY environment variable.")
else:
# Gemini Model Initialization
MODEL_ID = "gemini-2.0-flash-exp"
genai.configure(api_key=api_key)
model = genai.GenerativeModel(MODEL_ID)
# Correct initialization of the 'chat' object
chat = model.start_chat()
st.title("📚 AI-Powered Document Analyzer")
with st.expander("📖 **What is this app about?**"):
st.write("""
The **AI-Powered Document Analyzer** app is an AI-powered tool designed to help users extract valuable insights from any PDF document.
By leveraging **Gemini 2.0's Flash Experimental Model**, this intelligent system allows users to interactively engage with their documents,
making research and information retrieval more efficient.
""")
# Upload Section
st.header("Upload Document")
uploaded_file = st.file_uploader("Upload a PDF file to be analyzed", type=["pdf"])
def extract_text_from_pdf(file):
pdf_reader = PdfReader(file)
return "\n".join([page.extract_text() for page in pdf_reader.pages if page.extract_text()])
def extract_keywords(text, num_keywords=10):
words = re.findall(r'\b\w{4,}\b', text.lower()) # Extract words with 4+ letters
common_words = set("the and for with from this that have will are was were been has".split()) # Stop words
filtered_words = [word for word in words if word not in common_words]
most_common = Counter(filtered_words).most_common(num_keywords)
return [word for word, _ in most_common]
def generate_suggested_questions(keywords):
"""Generate sample questions based on extracted keywords."""
questions = []
for keyword in keywords:
questions.append(f"What is the significance of {keyword} in the document?")
questions.append(f"Can you summarize the document's section on {keyword}?")
return questions
if uploaded_file:
document_text = extract_text_from_pdf(uploaded_file)
st.session_state["document_text"] = document_text
st.success("Document uploaded successfully!")
# Display Keyword Insights
st.header("🔑 Key Topic Insights")
keywords = extract_keywords(document_text)
st.write(", ".join(keywords))
# Generate Suggested Questions
st.session_state["suggested_questions"] = generate_suggested_questions(keywords)
else:
st.session_state.pop("document_text", None) # Remove document text if no file is uploaded
st.session_state.pop("suggested_questions", None)
# Question-Answering Section
if "document_text" in st.session_state:
st.header("Ask AI About Your Document")
# Handle the selected question from buttons
if "selected_question" not in st.session_state:
st.session_state["selected_question"] = ""
def ask_ai(question):
"""Process user question with the uploaded document."""
try:
prompt = f"Analyze the following document and answer: {question}\n\nDocument Content:\n{st.session_state['document_text'][:5000]}"
response = chat.send_message(prompt) # Sending the message to 'chat'
return response.text
except Exception as e:
return f"Error: {e}"
# Text input for entering a question
selected_question = st.text_input(
"Enter your question about the document contents:",
value=st.session_state["selected_question"]
)
# Suggested Questions Section (between input and button)
if "suggested_questions" in st.session_state:
st.write("💡 **Suggested Questions:**")
# Limit to 5 questions
limited_suggested_questions = st.session_state["suggested_questions"][:5]
num_columns = len(limited_suggested_questions)
# Display in a row with smaller text
cols = st.columns(num_columns)
for i, question in enumerate(limited_suggested_questions):
with cols[i]:
if st.button(f"🔹 {question}", key=f"btn_{i}"):
st.session_state["selected_question"] = question
# Generate Answer Button
if st.button("Generate Answer") and selected_question:
with st.spinner("AI is reading the document..."):
response = ask_ai(selected_question)
st.markdown(f"**Response:** \n {response}")
else:
st.warning("Please upload a document to proceed.")
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