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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=("AIzaSyC-R8zVkX4m89Xx7j2mjCIH4S-wgHuQkvY")) | |
| #alternative key | |
| #genai.configure(api_key=("AIzaSyC-R8zVkX4m89Xx7j2mjCIH4S-wgHuQkvY")) | |
| # 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) | |