LeanStartupAgent_app_public.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "zC0llW5wEH-M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bb5d28d-f679-4aa0-85f6-13751a1b7c52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for validators (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q streamlit"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!npm install localtunnel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJ7OjCX2EO9p",
        "outputId": "0cf170d4-894b-45ec-e5e7-8c400619f549"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K\u001b[?25h\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m \u001b[0m\u001b[35msaveError\u001b[0m ENOENT: no such file or directory, open '/content/package.json'\n",
            "\u001b[K\u001b[?25h\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[34;40mnotice\u001b[0m\u001b[35m\u001b[0m created a lockfile as package-lock.json. You should commit this file.\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m \u001b[0m\u001b[35menoent\u001b[0m ENOENT: no such file or directory, open '/content/package.json'\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No description\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No repository field.\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No README data\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m\u001b[35m\u001b[0m content No license field.\n",
            "\u001b[0m\n",
            "+ localtunnel@2.0.2\n",
            "added 22 packages from 22 contributors and audited 22 packages in 2.807s\n",
            "\n",
            "3 packages are looking for funding\n",
            "  run `npm fund` for details\n",
            "\n",
            "found \u001b[92m0\u001b[0m vulnerabilities\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgqgRN0IOLBN",
        "outputId": "71bbe872-9d1d-472a-9ff8-ab710daaa5aa"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting langchain\n",
            "  Downloading langchain-0.0.168-py3-none-any.whl (817 kB)\n",
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            "\u001b[?25hRequirement already satisfied: PyYAML>=5.4.1 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0)\n",
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            "Collecting aiohttp<4.0.0,>=3.8.3 (from langchain)\n",
            "  Downloading aiohttp-3.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
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            "\u001b[?25hCollecting async-timeout<5.0.0,>=4.0.0 (from langchain)\n",
            "  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
            "Collecting dataclasses-json<0.6.0,>=0.5.7 (from langchain)\n",
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            "Collecting openapi-schema-pydantic<2.0,>=1.2 (from langchain)\n",
            "  Downloading openapi_schema_pydantic-1.2.4-py3-none-any.whl (90 kB)\n",
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            "Collecting multidict<7.0,>=4.5 (from aiohttp<4.0.0,>=3.8.3->langchain)\n",
            "  Downloading multidict-6.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n",
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            "  Downloading yarl-1.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (268 kB)\n",
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            "\u001b[?25hCollecting frozenlist>=1.1.1 (from aiohttp<4.0.0,>=3.8.3->langchain)\n",
            "  Downloading frozenlist-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (149 kB)\n",
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            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
            "Collecting marshmallow<4.0.0,>=3.3.0 (from dataclasses-json<0.6.0,>=0.5.7->langchain)\n",
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            "\u001b[?25hCollecting marshmallow-enum<2.0.0,>=1.5.1 (from dataclasses-json<0.6.0,>=0.5.7->langchain)\n",
            "  Downloading marshmallow_enum-1.5.1-py2.py3-none-any.whl (4.2 kB)\n",
            "Collecting typing-inspect>=0.4.0 (from dataclasses-json<0.6.0,>=0.5.7->langchain)\n",
            "  Downloading typing_inspect-0.8.0-py3-none-any.whl (8.7 kB)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2,>=1->langchain) (4.5.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2022.12.7)\n",
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            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.3.0->dataclasses-json<0.6.0,>=0.5.7->langchain) (23.1)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect>=0.4.0->dataclasses-json<0.6.0,>=0.5.7->langchain)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: mypy-extensions, multidict, marshmallow, frozenlist, async-timeout, yarl, typing-inspect, openapi-schema-pydantic, marshmallow-enum, aiosignal, dataclasses-json, aiohttp, langchain\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 dataclasses-json-0.5.7 frozenlist-1.3.3 langchain-0.0.168 marshmallow-3.19.0 marshmallow-enum-1.5.1 multidict-6.0.4 mypy-extensions-1.0.0 openapi-schema-pydantic-1.2.4 typing-inspect-0.8.0 yarl-1.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_lIXMerOZKc",
        "outputId": "fce5e2f7-58d0-45b6-b45f-1c95e796bd6b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting openai\n",
            "  Downloading openai-0.27.6-py3-none-any.whl (71 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai) (4.65.0)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.4)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (23.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (4.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.1)\n",
            "Installing collected packages: openai\n",
            "Successfully installed openai-0.27.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VVzhzO-deA09"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install google-search-results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHhzPExJdiu-",
        "outputId": "965dd019-72f0-42d8-ef1f-8bc7a061f73b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting google-search-results\n",
            "  Downloading google_search_results-2.4.2.tar.gz (18 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from google-search-results) (2.27.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->google-search-results) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->google-search-results) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->google-search-results) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->google-search-results) (3.4)\n",
            "Building wheels for collected packages: google-search-results\n",
            "  Building wheel for google-search-results (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for google-search-results: filename=google_search_results-2.4.2-py3-none-any.whl size=32002 sha256=b909b781079cdb82a9bc8242cf808681c24d5eee3c1b1e2b20709f61cf9f1515\n",
            "  Stored in directory: /root/.cache/pip/wheels/d3/b2/c3/03302d12bb44a2cdff3c9371f31b72c0c4e84b8d2285eeac53\n",
            "Successfully built google-search-results\n",
            "Installing collected packages: google-search-results\n",
            "Successfully installed google-search-results-2.4.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "import streamlit as st\n",
        "from langchain.llms import OpenAI\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.chains import LLMChain, SequentialChain \n",
        "from langchain.memory import ConversationBufferMemory\n",
        "from langchain.utilities import WikipediaAPIWrapper \n",
        "from langchain.llms import OpenAI\n",
        "from langchain.utilities import SerpAPIWrapper\n",
        "from langchain.agents import initialize_agent, Tool\n",
        "from langchain.agents import load_tools\n",
        "from langchain.agents import AgentType\n",
        "\n",
        "serpapi_key = 'your api key'\n",
        "open_api_key = 'your api key'\n",
        "\n",
        "st.title(\"LeanStartupAgent\")\n",
        "\n",
        "#define company and project\n",
        "\n",
        "job_to_be_done = st.text_input('Describe the core job to be done')\n",
        "customer_description = st.text_input('Describe your customer segment')\n",
        "\n",
        "clicked = st.button('Click me')\n",
        "\n",
        "if clicked:\n",
        "    st.write('Button clicked! Performing an operation...')\n",
        "    # Place the code that should only execute after click here\n",
        "    llm = OpenAI(openai_api_key=open_api_key, temperature=0.8)\n",
        "    hardest_template = PromptTemplate(\n",
        "      input_variables = ['customer_description','job_to_be_done'], \n",
        "      template='You are {customer_description}. What is the hardest part about {job_to_be_done}?')\n",
        "    hardest_chain = LLMChain(llm=llm, prompt=hardest_template, verbose=True, output_key='hardest_part')\n",
        "    hardest = hardest_chain.run(customer_description=customer_description, job_to_be_done=job_to_be_done)\n",
        "    st.markdown('**Agent core painpoint =**')\n",
        "    st.write(hardest)\n",
        "    value_proposition_template = PromptTemplate(\n",
        "      input_variables = ['hardest_part'], \n",
        "      template='Create a unique value proposition for a startup that solves the problem of {hardest_part}')\n",
        "    value_proposition_chain = LLMChain(llm=llm, prompt=value_proposition_template, verbose=True, output_key='value_proposition')\n",
        "    valueproposition = value_proposition_chain.run(hardest_part=hardest)\n",
        "    st.markdown('**Agent Value proposition =**')\n",
        "    st.write(valueproposition)\n",
        "    canvas_template = PromptTemplate(\n",
        "      input_variables = ['value_proposition'], \n",
        "      template='Provide the value proposition canvas for {value_proposition}. You should describe the following elements: customer jobs, custome pains, customer gains, products and services, pain relievers, gain creators')\n",
        "    canvas_chain = LLMChain(llm=llm, prompt=canvas_template, verbose=True, output_key='canvas')\n",
        "    canvas = canvas_chain.run(value_proposition=valueproposition)\n",
        "    st.markdown('**Agent description of value proposition canvas =**')\n",
        "    st.write(canvas)\n",
        "    competitors_template = PromptTemplate(\n",
        "      input_variables = ['value_proposition'], \n",
        "      template='Find three existing startup companies that have a description that is very similar to the following description: {value_proposition}. For each startup, provide company name, location and website.')\n",
        "    competitors_chain = LLMChain(llm=llm, prompt=competitors_template, verbose=True, output_key='competitors')\n",
        "    competitors = competitors_chain.run(value_proposition=valueproposition)\n",
        "    st.markdown('**Agent identification of competitors =**')\n",
        "    st.write(competitors)\n",
        "    competitorsselect = competitors[2:]\n",
        "    competitors_split1 = competitorsselect.split('\\n')\n",
        "    competitors_list = list()\n",
        "    for competitor in competitors_split1:\n",
        "      competitor_split = competitor.split(',')\n",
        "      competitors_list.append(competitor_split[0][2:])\n",
        "    competitors_list\n",
        "    search = SerpAPIWrapper(serpapi_api_key = serpapi_key)\n",
        "    search_tool = [Tool(\n",
        "        name=\"Intermediate Answer\",\n",
        "        func=search.run,\n",
        "        description=\"useful for when you need to ask with search\")]\n",
        "    self_ask_with_search = initialize_agent(search_tool, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)\n",
        "    for i in range(len(competitors_list)):\n",
        "      company = competitors_list[i]\n",
        "      searchstring = \"Provide company information for \" + company + \". Include company description and founders\"\n",
        "      result = self_ask_with_search.run(searchstring)\n",
        "      st.markdown('**Additional information competitor =**')\n",
        "      st.write(result)\n",
        "    hypotheses_template = PromptTemplate(\n",
        "      input_variables = ['value_proposition', 'canvas'], \n",
        "      template='Generate a list of hypotheses for testing the following value proposition: {value_proposition}. You should rely on information from the value proposition canvas: {canvas}. Include at least 3 hypotheses. Each hypothesis should focus on the feasibility or viability of the business model. Here are some potential topics for hypotheses testing: willingness to pay, preferred distribution model, ultimate customer segmet, etc.')\n",
        "    hypotheses_chain = LLMChain(llm=llm, prompt=hypotheses_template, verbose=True, output_key='hypotheses')\n",
        "    hypotheses = hypotheses_chain.run(value_proposition=valueproposition, canvas=canvas)\n",
        "    st.markdown('**Agent hypotheses =**')\n",
        "    st.write(hypotheses)\n",
        "else:\n",
        "    st.write('Please click the button to perform an operation')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RZR5Do3RSZuj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e14029f-01b6-4ce1-c9fb-019fbe80bf52"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run app.py &>/content/logs.txt &"
      ],
      "metadata": {
        "id": "sD7V4nL_ERqh"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!npx localtunnel --port 8501"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60vGV1rdEd6X",
        "outputId": "1763e583-ce51-4284-f011-ed75c0811835"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K\u001b[?25hnpx: installed 22 in 1.527s\n",
            "your url is: https://slimy-peas-stay.loca.lt\n",
            "^C\n"
          ]
        }
      ]
    }
  ]
}