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60d3f7a 6fe12fb 60d3f7a 19e7a62 60d3f7a 2fb3630 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | 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 sampling
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 (nucleus sampling)
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")
# WB_SEARCH = True
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")
# col1, col2 = st.columns([6, 1], gap="small")
# column 1
# with col1:
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"])
# prompt = st.chat_input("Say something...")
if prompt:= st.chat_input("Say something..."):
# Display user message
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)
# st.session_state.messages.append({"role": "assistant", "content": response})
if option == "Web Search":
st.session_state.messages.append([{"role": "user", "content" : prompt}])
response, sources = search_web(prompt)
# asnswer = response
# st.chat_message("assistant").markdown(f"{response}\n\n###Sources\n{'\n'.join([source for source in sources])}")
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)
# asnswer = response
# st.chat_message("assistant").markdown(f"{response}\n\n###Sources\n{'\n'.join([source for source in sources])}")
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)
# print(file)
with st.chat_message("assistant"):
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
# # Slider in column 2
# with col2:
# temperature = st.slider("Temperature", 0.0, 1.0, 0.0)
# top_k = st.slider("Top k", 0.0, 100.0, 40.0)
# top_p = st.slider("Top p", 0.0, 1.0, 0.95)
if __name__ == "__main__":
main() |