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import streamlit as st
import google.generativeai as genai
import os
import PyPDF2 as pdf
from dotenv import load_dotenv
load_dotenv()
genai.configure(api_key=("AIzaSyDziGvuT1woHnH4_S3L_zQZV55Yj-123A8"))
#alternative key
#genai.configure(api_key=("AIzaSyAr3d_7fp0wMxuUrnf_tATknu_TRPKDdxg"))
# gemini function for general content generation
def get_gemini_response(input):
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(input)
return response
# convert pdf to text
def input_pdf_text(uploaded_file):
reader = pdf.PdfReader(uploaded_file)
text = ""
for page in range(len(reader.pages)):
page = reader.pages[page]
text += str(page.extract_text())
return text
# malware detection function
def detect_malware(input_text):
malware_prompt = f"""
### As a cybersecurity expert, your task is to analyze the following text for any indications of malware.
### Text:
{input_text}
### Analysis Output:
1. Identify any potential malware-related content.
2. Explain the reasoning behind your identification.
3. Provide recommendations for mitigating any identified risks.
"""
response = get_gemini_response(malware_prompt)
return response
# chatbot function
def chatbot_response(user_input):
chatbot_prompt = f"""
### You are an intelligent and friendly chatbot. Engage in a meaningful conversation with the user.
### User Input:
{user_input}
### Chatbot Response:
"""
response = get_gemini_response(chatbot_prompt)
return response
# Function to parse and display response content
def display_response_content(response):
st.subheader("Response Output")
if response and response.candidates:
response_content = response.candidates[0].content.parts[0].text if response.candidates[0].content.parts else ""
sections = response_content.split('###')
for section in sections:
if section.strip():
section_lines = section.split('\n')
section_title = section_lines[0].strip()
section_body = '\n'.join(line.strip() for line in section_lines[1:] if line.strip())
if section_title:
st.markdown(f"**{section_title}**")
if section_body:
st.write(section_body)
else:
st.write("No response received from the model.")
## Streamlit App
st.title("AI-Powered Security and Chatbot System")
st.text("Use the AI system for malware detection and Awaring yourself.")
# Tabs for different functionalities
tab1, tab2 = st.tabs(["Malware Detection", "Chatbot"])
with tab1:
st.header("Malware Detection")
uploaded_file = st.file_uploader("Upload a file for malware detection", type="pdf", help="Please upload a PDF file.")
submit_malware = st.button('Analyze for Malware')
if submit_malware:
if uploaded_file is not None:
text = input_pdf_text(uploaded_file)
response = detect_malware(text)
# Parse and display response in a structured way
display_response_content(response)
with tab2:
st.header("Chatbot")
user_input = st.text_input("Type your message here")
submit_chat = st.button('Send')
if submit_chat:
if user_input:
response = chatbot_response(user_input)
# Parse and display response in a structured way
display_response_content(response)
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