import os from dotenv import load_dotenv import time import streamlit as st from langchain_groq import ChatGroq from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser load_dotenv() groq_api_key= os.getenv("GROQ_API_KEY") p1 = os.getenv("pmpt1") p2 = os.getenv("pmpt2") p3 = os.getenv("pmpt3") p4 = os.getenv("pmpt4") p5 = os.getenv("pmpt5") p6 = os.getenv("pmpt6") p7 = os.getenv("pmpt7") p8 = os.getenv("pmpt8") p9 = os.getenv("pmpt9") p10 = os.getenv("pmpt10") p11 = os.getenv("pmpt11") p12 = os.getenv("pmpt12") p13 = os.getenv("pmpt13") p14 = os.getenv("pmpt14") p15 = os.getenv("pmpt15") p16 = os.getenv("pmpt16") p17 = os.getenv("pmpt17") p18 = os.getenv("pmpt18") p19 = os.getenv("pmpt19") p20 = os.getenv("pmpt20") p21 = os.getenv("pmpt21") p22 = os.getenv("pmpt22") p23 = os.getenv("pmpt23") p24 = os.getenv("pmpt24") p25 = os.getenv("pmpt25") prompt1 = ChatPromptTemplate.from_messages([("system",p1),("user", "Question:{query1}")]) prompt2 = ChatPromptTemplate.from_messages([("system",p2),("user", "Question:{query1}")]) prompt3 = ChatPromptTemplate.from_messages([("system",p3),("user", "Question:{query1}")]) prompt4 = ChatPromptTemplate.from_messages([("system",p4),("user", "Question:{query1}")]) prompt5 = ChatPromptTemplate.from_messages([("system",p5),("user", "Question:{query1}")]) prompt6 = ChatPromptTemplate.from_messages([("system",p6), ("user", "Question:{query1}")]) prompt7 = ChatPromptTemplate.from_messages([("system",p7), ("user", "Question:{query1}")]) prompt8 = ChatPromptTemplate.from_messages([("system",p8), ("user", "Question:{query1}")]) prompt9 = ChatPromptTemplate.from_messages([("system",p9), ("user", "Question:{query1}")]) prompt10 = ChatPromptTemplate.from_messages([("system", p10), ("user", "Question:{query1}")]) prompt11 = ChatPromptTemplate.from_messages([("system", p11), ("user", "Question:{query1}")]) prompt12 = ChatPromptTemplate.from_messages([("system", p12), ("user", "Question:{query1}")]) prompt13 = ChatPromptTemplate.from_messages([("system", p13), ("user", "Question:{query1}")]) prompt14 = ChatPromptTemplate.from_messages([("system", p14), ("user", "Question:{query1}")]) prompt15 = ChatPromptTemplate.from_messages([("system", p15), ("user", "Question:{query1}")]) prompt16 = ChatPromptTemplate.from_messages([("system", p16), ("user", "Question:{query1}")]) prompt17 = ChatPromptTemplate.from_messages([("system", p17), ("user", "Question:{query1}")]) prompt18 = ChatPromptTemplate.from_messages([("system", p18), ("user", "Question:{query1}")]) prompt19 = ChatPromptTemplate.from_messages([("system", p19), ("user", "Question:{query1}")]) prompt20 = ChatPromptTemplate.from_messages([("system", p20), ("user", "Question:{query1}")]) prompt21 = ChatPromptTemplate.from_messages([("system", p21), ("user", "Question:{query1}")]) prompt22 = ChatPromptTemplate.from_messages([("system", p22), ("user", "Question:{query1}")]) prompt23 = ChatPromptTemplate.from_messages([("system", p23), ("user", "Question:{query1}")]) prompt24 = ChatPromptTemplate.from_messages([("system", p24), ("user", "Question:{query1}")]) prompt25 = ChatPromptTemplate.from_messages([("system", p25), ("user", "Question:{query1}")]) llm1 = ChatGroq(model_name="llama3-70b-8192", groq_api_key=groq_api_key) output_parser = StrOutputParser() chain1 = prompt1| llm1| output_parser chain2 = prompt2| llm1| output_parser chain3 = prompt3| llm1| output_parser chain4 = prompt4| llm1| output_parser chain5 = prompt5| llm1| output_parser chain6 = prompt6| llm1| output_parser chain7 = prompt7| llm1| output_parser chain8 = prompt8| llm1| output_parser chain9 = prompt9| llm1| output_parser chain10 = prompt10| llm1| output_parser chain11 = prompt11| llm1| output_parser chain12 = prompt12| llm1| output_parser chain13 = prompt13| llm1| output_parser chain14 = prompt14| llm1| output_parser chain15 = prompt15| llm1| output_parser chain16 = prompt16| llm1| output_parser chain17 = prompt17| llm1| output_parser chain18 = prompt18| llm1| output_parser chain19 = prompt19| llm1| output_parser chain20 = prompt20| llm1| output_parser chain21 = prompt21| llm1| output_parser chain22 = prompt22| llm1| output_parser chain23 = prompt23| llm1| output_parser chain24 = prompt24| llm1| output_parser chain25 = prompt25| llm1| output_parser def generate_ai_content(thinking_type, usr_ip): if thinking_type == "Analytical Thinking": return chain1.invoke({"query1": usr_ip}) elif headline == "Creative Thinking":return chain2.invoke({"query1": usr_ip}) elif headline == "Critical Thinking": return chain3.invoke({"query1": usr_ip}) elif headline == "Logical Thinking": return chain4.invoke({"query1": usr_ip}) elif headline == "Lateral Thinking": return chain5.invoke({"query1": usr_ip}) elif headline == "Divergent Thinking": return chain6.invoke({"query1": usr_ip}) elif headline == "Convergent Thinking": return chain7.invoke({"query1": usr_ip}) elif headline == "Empathetic Thinking": return chain8.invoke({"query1": usr_ip}) elif headline == "Systems Thinking": return chain9.invoke({"query1": usr_ip}) elif headline == "Intuitive Thinking": return chain10.invoke({"query1": usr_ip}) elif headline == "Strategic Thinking": return chain11.invoke({"query1": usr_ip}) elif headline == "Collaborative Thinking": return chain12.invoke({"query1": usr_ip}) elif headline == "Reverse Thinking": return chain13.invoke({"query1": usr_ip}) elif headline == "Practical Thinking": return chain14.invoke({"query1": usr_ip}) elif headline == "Mind Mapping": return chain15.invoke({"query1": usr_ip}) elif headline == "Trial-and-Error Thinking": return chain16.invoke({"query1": usr_ip}) elif headline == "Root Cause Analysis": return chain17.invoke({"query1": usr_ip}) elif headline == "Optimistic Thinking": return chain18.invoke({"query1": usr_ip}) elif headline == "Pessimistic Thinking": return chain19.invoke({"query1": usr_ip}) elif headline == "Abstract Thinking": return chain20.invoke({"query1": usr_ip}) elif headline == "Habitual Thinking": return chain21.invoke({"query1": usr_ip}) elif headline == "Scenario Thinking": return chain22.invoke({"query1": usr_ip}) elif headline == "Mathematical Thinking": return chain23.invoke({"query1": usr_ip}) elif headline == "Ethical Thinking": return chain24.invoke({"query1": usr_ip}) elif headline == "Design Thinking": return chain25.invoke({"query1": usr_ip}) st.title("Think AI") st.text("Think AI is designed to explore all the posible ways to approch a problem to find the perfect solution.") st.write("### Ask anything") col1, col2 = st.columns([4, 1]) with col1: user_input = st.text_area("Enter your text:", key="input_text", height=68) with col2: submit = st.button("Submit") if submit and user_input.strip(): counter = 0 st.write("---") st.write("### Generated Content") headlines = ["Analytical Thinking", "Creative Thinking", "Critical Thinking","Logical Thinking", "Lateral Thinking","Divergent Thinking", "Convergent Thinking", "Empathetic Thinking", "Systems Thinking", "Intuitive Thinking","Strategic Thinking", "Collaborative Thinking", "Reverse Thinking", "Practical Thinking", "Mind Mapping","Trial-and-Error Thinking", "Root Cause Analysis", "Optimistic Thinking", "Pessimistic Thinking", "Abstract Thinking", "Habitual Thinking", "Scenario Thinking", "Mathematical Thinking", "Ethical Thinking", "Design Thinking", ] for headline in headlines: if counter >=5: time.sleep(3) counter =0 st.write(f"#### {headline}") ai_content = generate_ai_content(headline, user_input) st.markdown(ai_content,unsafe_allow_html=True) # st.text_area(f" ", value=ai,key=headline) counter+=1 st.markdown( """
""", unsafe_allow_html=True ) # End the box container st.markdown('', unsafe_allow_html=True)