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import streamlit as st
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import random
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from langchain_community.chat_models import ChatOpenAI
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from langchain.schema import HumanMessage, SystemMessage
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from langchain_community.document_loaders import PyPDFLoader, TextLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_community.vectorstores import FAISS
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from langchain.chains import RetrievalQA
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import os
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from dotenv import load_dotenv
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import tempfile
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load_dotenv()
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AI71_BASE_URL = "https://api.ai71.ai/v1/"
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AI71_API_KEY = "api71-api-92fc2ef9-9f3c-47e5-a019-18e257b04af2"
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chat = ChatOpenAI(
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model="tiiuae/falcon-180B-chat",
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api_key=AI71_API_KEY,
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base_url=AI71_BASE_URL,
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streaming=True,
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)
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embeddings = HuggingFaceEmbeddings()
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PREDEFINED_TOPICS = [
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"Quantum Computing", "Artificial Intelligence Ethics", "Blockchain Technology",
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"Neuroscience", "Climate Change Mitigation", "Space Exploration",
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"Renewable Energy", "Genetic Engineering", "Cybersecurity",
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"Machine Learning", "Nanotechnology", "Robotics",
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"Virtual Reality", "Augmented Reality", "Internet of Things",
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"5G Technology", "Autonomous Vehicles", "Bioinformatics",
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"Cloud Computing", "Data Science", "Artificial General Intelligence",
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"Quantum Cryptography", "3D Printing", "Smart Cities",
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"Biotechnology", "Fusion Energy", "Sustainable Agriculture",
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"Space Tourism", "Quantum Sensors", "Brain-Computer Interfaces",
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"Personalized Medicine", "Synthetic Biology", "Exoplanets",
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"Dark Matter", "CRISPR Technology", "Quantum Internet",
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"Deep Learning", "Edge Computing", "Humanoid Robots",
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"Drone Technology", "Quantum Supremacy", "Neuromorphic Computing",
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"Asteroid Mining", "Bionic Implants", "Smart Materials",
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"Quantum Dots", "Lab-grown Meat", "Vertical Farming",
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"Hyperloop Transportation", "Molecular Nanotechnology", "Quantum Metrology",
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"Artificial Photosynthesis", "Cognitive Computing", "Swarm Robotics",
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"Metamaterials", "Neuroplasticity", "Quantum Machine Learning",
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"Green Hydrogen", "Organ-on-a-Chip", "Bioprinting",
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"Plasma Physics", "Quantum Simulation", "Soft Robotics",
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"Geoengineering", "Exoskeletons", "Programmable Matter",
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"Graphene Applications", "Quantum Sensing", "Neuralink",
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"Holographic Displays", "Quantum Error Correction", "Synthetic Genomes",
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"Carbon Capture and Storage", "Quantum Memory", "Organoids",
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"Artificial Synapses", "Quantum Imaging", "Biosensors",
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"Memristors", "Quantum Annealing", "DNA Data Storage",
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"Cultured Meat", "Quantum Radar", "Neuromorphic Hardware",
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"Quantum Entanglement", "Phytomining", "Biohacking",
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"Topological Quantum Computing", "Neuroprosthetics", "Optogenetics",
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"Quantum Gravity", "Molecular Machines", "Biomimicry",
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"Quantum Teleportation", "Neurogenesis", "Bioelectronics",
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"Quantum Tunneling", "Tissue Engineering", "Bioremediation",
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"Quantum Photonics", "Synthetic Neurobiology", "Nanomedicine",
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"Quantum Biology", "Biogeochemistry", "Molecular Gastronomy",
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"Quantum Thermodynamics", "Nutrigenomics", "Biomechatronics",
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"Quantum Chemistry", "Psychoneuroimmunology", "Nanophotonics",
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"Quantum Optics", "Neuroeconomics", "Bionanotechnology"
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]
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def process_document(file):
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with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.name)[1]) as temp_file:
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temp_file.write(file.getvalue())
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temp_file_path = temp_file.name
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if file.name.endswith('.pdf'):
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loader = PyPDFLoader(temp_file_path)
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else:
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loader = TextLoader(temp_file_path)
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documents = loader.load()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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texts = text_splitter.split_documents(documents)
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vectorstore = FAISS.from_documents(texts, embeddings)
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retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
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qa_chain = RetrievalQA.from_chain_type(
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llm=chat,
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chain_type="stuff",
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retriever=retriever,
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return_source_documents=True
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)
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os.unlink(temp_file_path)
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return qa_chain
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def get_sherlock_analysis(topic, qa_chain=None):
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system_prompt = """
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You are Sherlock Holmes, the world's greatest detective and master of observation and deduction.
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Your task is to provide an in-depth analysis of the given topic, offering unique insights on how to approach learning it from the ground up.
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Your analysis should:
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1. Break down the topic into its fundamental components.
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2. Identify key concepts and their relationships.
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3. Suggest a structured approach to learning, starting from first principles.
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4. Highlight potential challenges and how to overcome them.
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5. Provide a unique point of view that encourages critical thinking.
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Your response should be detailed, insightful, and encourage a deep understanding of the subject.
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"""
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if qa_chain:
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result = qa_chain({"query": f"Provide a Sherlock Holmes style analysis of the topic: {topic}"})
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response = result['result']
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else:
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messages = [
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SystemMessage(content=system_prompt),
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HumanMessage(content=f"Analyze the following topic: {topic}")
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]
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response = chat.invoke(messages).content
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return response
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def chunk_text(text, max_chunk_size=4000):
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chunks = []
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current_chunk = ""
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for sentence in text.split(". "):
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if len(current_chunk) + len(sentence) < max_chunk_size:
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current_chunk += sentence + ". "
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else:
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chunks.append(current_chunk)
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current_chunk = sentence + ". "
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if current_chunk:
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chunks.append(current_chunk)
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return chunks
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def main():
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st.set_page_config(page_title="S.H.E.R.L.O.C.K. Observation", page_icon="🔍", layout="wide")
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st.title("🕵️ S.H.E.R.L.O.C.K. Observation")
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st.markdown("*Uncover the depths of any subject with the keen insight of Sherlock Holmes*")
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col1, col2 = st.columns([2, 1])
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with col2:
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st.subheader("Choose Your Method")
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method = st.radio("Select input method:", ["Enter Topic", "Upload Document", "Choose from List"])
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if method == "Enter Topic":
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topic = st.text_input("Enter your topic of interest:")
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elif method == "Upload Document":
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uploaded_file = st.file_uploader("Upload a document (PDF or TXT)", type=["pdf", "txt"])
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if uploaded_file:
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topic = uploaded_file.name
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else:
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topic = st.selectbox("Choose a topic:", PREDEFINED_TOPICS)
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if st.button("Analyze", key="analyze_button"):
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if method == "Upload Document" and uploaded_file:
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qa_chain = process_document(uploaded_file)
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analysis = get_sherlock_analysis(topic, qa_chain)
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elif topic:
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analysis = get_sherlock_analysis(topic)
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else:
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st.warning("Please provide a topic or upload a document.")
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return
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col1.markdown("## Sherlock's Analysis")
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chunks = chunk_text(analysis)
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for chunk in chunks:
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col1.markdown(chunk)
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st.sidebar.image("https://upload.wikimedia.org/wikipedia/commons/c/cd/Sherlock_Holmes_Portrait_Paget.jpg", use_column_width=True)
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st.sidebar.title("About S.H.E.R.L.O.C.K. Observation")
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st.sidebar.markdown("""
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S.H.E.R.L.O.C.K. Observation is your personal detective for any subject.
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It provides:
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- In-depth analysis of topics
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- Unique perspectives on learning approaches
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- First principles breakdown of subjects
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- Critical thinking encouragement
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Let Sherlock guide you through the intricacies of any field of study!
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""")
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st.sidebar.markdown("---")
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st.sidebar.markdown("Powered by Falcon-180B and Streamlit")
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if __name__ == "__main__":
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main() |
