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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}) |