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| import streamlit as st | |
| import requests | |
| from bs4 import BeautifulSoup | |
| import g4f | |
| # Set the title of the app | |
| st.title("Chat with GPT-4 Free") | |
| # Initialize chat history in session state | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| # Dropdown for model selection | |
| model_options = { | |
| "GPT-3.5 Turbo": g4f.models.gpt_3_5_turbo, | |
| "GPT-4": g4f.models.gpt_4, | |
| "GPT-4o": g4f.models.gpt_4o, | |
| } | |
| selected_model = st.sidebar.selectbox("Select Model", list(model_options.keys())) | |
| # Toggle for internet search functionality | |
| search_enabled = st.sidebar.checkbox("Enable Internet Search") | |
| # Function to perform a Google search | |
| def google_search(query): | |
| headers = { | |
| 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36' | |
| } | |
| # Construct the search URL | |
| url = f"https://www.google.com/search?q={query}" | |
| # Send the request to Google | |
| response = requests.get(url, headers=headers) | |
| # Parse the HTML content | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| # Find all search result containers | |
| results = soup.find_all('div', class_='g') | |
| # Extract titles and summaries | |
| search_results = [] | |
| for result in results: | |
| title = result.find('h3') | |
| summary = result.find('span', class_='aCOpRe') | |
| if title and summary: | |
| search_results.append({ | |
| 'title': title.text, | |
| 'summary': summary.text | |
| }) | |
| return search_results | |
| # Function to generate responses from g4f | |
| def generate_response(prompt, model, search=False): | |
| if search: | |
| # Perform a web search and return results | |
| search_results = google_search(prompt) | |
| formatted_results = "\n".join([f"{result['title']}: {result['summary']}" for result in search_results]) | |
| return f"Search Results:\n{formatted_results}" if formatted_results else "No results found." | |
| response = g4f.ChatCompletion.create( | |
| model=model, | |
| messages=[{"role": "user", "content": prompt}], | |
| stream=True # Enable streaming for real-time response | |
| ) | |
| return response | |
| # Display chat messages from history | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Accept user input | |
| if prompt := st.chat_input("Type your message here..."): | |
| # Display user message in chat message container | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # Generate and display AI response | |
| with st.chat_message("assistant"): | |
| response = generate_response(prompt, model_options[selected_model], search=search_enabled) | |
| if isinstance(response, str): # Check if the response is a string | |
| st.markdown(response) | |
| # Add assistant's response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": response}) |