| import streamlit as st |
| from run_model import generate_response, generate_RAG_response |
| from web_search import search_web |
| from deep_research import perform_deep_research |
| import tempfile |
|
|
| st.set_page_config(page_title="Chat with AI", |
| page_icon="🚀",layout="wide") |
|
|
| def main(): |
| st.title("Chat with AI") |
| |
|
|
| with st.sidebar: |
| st.title("Tools") |
| |
| option = st.selectbox( |
| "Choose tools", |
| ("Simple Chat", "Web Search", "Upload PDF", "Deep Web Search"), |
| ) |
|
|
| temperature = st.slider( |
| label="Temperature (controls randomness)", |
| min_value=0.0, |
| max_value=2.0, |
| value=1.0, |
| step=0.01, |
| help="Lower = more deterministic, Higher = more random" |
| ) |
| |
| top_k = st.slider( |
| label="Top-k (limits to top K tokens by probability)", |
| min_value=0, |
| max_value=100, |
| value=50, |
| step=1, |
| help="0 = disable top-k filtering" |
| ) |
|
|
| |
| top_p = st.slider( |
| label="Top-p (nucleus sampling cutoff)", |
| min_value=0.0, |
| max_value=1.0, |
| value=0.9, |
| step=0.01, |
| help="0.0 = conservative, 1.0 = more random" |
| ) |
|
|
| if option == "Upload PDF": |
| file = st.file_uploader(label="Uploaded file will provide context to LLM", type="pdf") |
| |
|
|
| if option == "Web Search": |
| st.write("Web Search Enabled for next query") |
| |
|
|
| if option == "None": |
| st.warning("You are not using any tool") |
|
|
| if option == "Deep Web Search": |
| st.write("Deep Web Research Enabled for next query") |
|
|
|
|
|
|
| |
|
|
| |
| |
| if "messages" not in st.session_state: |
| st.session_state.messages = [] |
| |
| for msg in st.session_state.messages: |
| with st.chat_message(msg["role"]): |
| st.markdown(msg["content"]) |
|
|
| |
|
|
| if prompt:= st.chat_input("Say something..."): |
| |
| st.chat_message("user").markdown(prompt) |
| st.session_state.messages.append({"role": "user", "content": prompt}) |
| if option == "Simple Chat": |
|
|
| response = generate_response(history=st.session_state.messages, temperature=temperature, top_k=top_k, top_p=top_p) |
| st.chat_message("assistant").markdown(response) |
| |
| |
| if option == "Web Search": |
| st.session_state.messages.append([{"role": "user", "content" : prompt}]) |
|
|
| response, sources = search_web(prompt) |
| |
| |
| with st.chat_message("assistant"): |
| st.markdown(f"{response}\n\n### Sources\n" + "\n".join(sources)) |
|
|
| if option == "Deep Web Search": |
| st.session_state.messages.append([{"role": "user", "content" : prompt}]) |
|
|
| response = perform_deep_research(prompt) |
| |
| |
| with st.chat_message("assistant"): |
| st.markdown(response) |
|
|
| if option == "Upload PDF": |
| st.session_state.messages.append([{"role": "user", "content" : prompt}]) |
|
|
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file: |
| tmp_file.write(file.read()) |
| tmp_path = tmp_file.name |
| print(tmp_path) |
| response = generate_RAG_response(prompt, tmp_path, st.session_state.messages) |
| |
| with st.chat_message("assistant"): |
| st.markdown(response) |
| |
| st.session_state.messages.append({"role": "assistant", "content": response}) |
| |
| |
| |
| |
| |
| |
| |
|
|
|
|
| if __name__ == "__main__": |
| main() |
