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| import requests | |
| import streamlit as st | |
| from layouts.mainlayout import mainlayout | |
| def upload_data(): | |
| # upload pdf | |
| upload_pdf = st.file_uploader("Upload PDF", type="pdf") | |
| if upload_pdf is not None: | |
| files = {"file": upload_pdf} | |
| with st.spinner("Uploading PDF..."): | |
| response = requests.post( | |
| "https://hemanthsai7-studybotapi.hf.space/api/upload", files=files | |
| ) | |
| if response.status_code == 200: | |
| st.success( | |
| f'{response.json()["message"][0]}. Vector Store created successfully!' | |
| ) | |
| st.session_state.uploaded_pdf=True | |
| else: | |
| st.error("Failed to upload PDF!") | |
| upload_data() | |
| with st.expander("What happens when I upload a PDF? 📑", expanded=True): | |
| st.info( | |
| """ | |
| - The PDF is uploaded to the backend server. ⚙️ | |
| - The PDF is converted into small chunks for faster processing. 🚀 | |
| - The chunks are broken down into tokens. A token is a single word or a group of words. 📝 | |
| - The tokens are converted into embedding vectors. 📊 | |
| - The embedding vectors are stored in a vector store. 🗄️ | |
| """, | |
| icon="ℹ️", | |
| ) | |
| st.divider() | |
| if "uploaded_pdf" in st.session_state.keys(): | |
| # chatbot | |
| st.subheader("Ask Studybot a question! 🤖") | |
| if "messages" not in st.session_state.keys(): | |
| st.session_state.messages = [ | |
| { | |
| "role": "assistant", | |
| "content": "What's troubling you? Ask me a question right away!", | |
| } | |
| ] | |
| # Display or clear chat messages | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.write(message["content"]) | |
| def clear_chat_history(): | |
| st.session_state.messages = [ | |
| { | |
| "role": "assistant", | |
| "content": "What's troubling you? Ask me a question right away!", | |
| } | |
| ] | |
| st.sidebar.button("Clear Chat History", on_click=clear_chat_history) | |
| def generate_mistral_response(question: str): | |
| for dict_message in st.session_state.messages: | |
| if dict_message["role"] == "user": | |
| question = dict_message["content"] | |
| answer = requests.post( | |
| "https://hemanthsai7-studybotapi.hf.space/api/inference", | |
| json={"promptMessage": question}, | |
| ).json() | |
| return answer | |
| # User-provided prompt | |
| if prompt := st.chat_input( | |
| disabled=not st.session_state.messages[-1]["role"] == "assistant", | |
| placeholder="Hello, please ask me a question! 🤖"): | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.write(prompt) | |
| # ask question | |
| st.write(st.session_state) | |
| # Generate a new response if last message is not from assistant | |
| if st.session_state.messages[-1]["role"] != "assistant": | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| response = generate_mistral_response(prompt) | |
| placeholder = st.empty() | |
| full_response = "" | |
| for item in response: | |
| full_response += item | |
| placeholder.markdown(full_response) | |
| placeholder.markdown(full_response) | |
| message = {"role": "assistant", "content": full_response} | |
| st.session_state.messages.append(message) | |