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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)
import streamlit as st
import google.generativeai as genai
import os
from dotenv import load_dotenv
import PyPDF2 as pdf
import docx
import chardet
load_dotenv()
genai.configure(api_key=("AIzaSyDziGvuT1woHnH4_S3L_zQZV55Yj-123A8"))
# gemini function for general content generation
def get_gemini_response(input):
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(input)
return response
# Function to read text from different file types
def read_file_content(uploaded_file):
file_type = uploaded_file.type
if file_type == "application/pdf":
reader = pdf.PdfReader(uploaded_file)
text = ""
for page in range(len(reader.pages)):
page = reader.pages[page]
text += str(page.extract_text())
elif file_type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
doc = docx.Document(uploaded_file)
text = '\n'.join([para.text for para in doc.paragraphs])
else:
# For other file types, assume it's a text file and try to read it as text
text = uploaded_file.read()
result = chardet.detect(text)
text = text.decode(result['encoding'])
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 friendly conversation.")
# 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=None, help="Please upload a file of any type.")
submit_malware = st.button('Analyze for Malware')
if submit_malware:
if uploaded_file is not None:
text = read_file_content(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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