port to 2.0
Browse files- README.md +5 -5
- app.py +1 -1
- requirements.txt +2 -3
- utils/haystack.py +40 -30
- utils/ui.py +2 -2
README.md
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##### A simple app to get an overview of what the Mastodon user has been posting about and their tone
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This is a demo just for fun 🥳
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This repo contains a streamlit application that given a Mastodon username, tells you what type of things they've been posting about lately, their tone, and the languages they use. It uses the LLM by OpenAI `
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It's been built with [Haystack](https://haystack.deepset.ai) using the [`
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https://user-images.githubusercontent.com/15802862/220464834-f42c038d-54b4-4d5e-8d59-30d95143b616.mov
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### Points of improvement
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Since we're using a generative model here, we need to be a bit creative with the prompt we provide it to minimize any hallucination or similar unwanted results. For this reason, I've tried to be a bit creative with the `
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If you try to run it yourself and find ways to make this app better, please feel free to create an issue/PR 🙌
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## To learn more about the
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## Installation and Running
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To run the bare application which does _nothing_:
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##### A simple app to get an overview of what the Mastodon user has been posting about and their tone
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This is a demo just for fun 🥳
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This repo contains a streamlit application that given a Mastodon username, tells you what type of things they've been posting about lately, their tone, and the languages they use. It uses the LLM by OpenAI `gpt-4`.
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It's been built with [Haystack](https://haystack.deepset.ai) using the [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) and by creating a [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder)
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https://user-images.githubusercontent.com/15802862/220464834-f42c038d-54b4-4d5e-8d59-30d95143b616.mov
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### Points of improvement
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Since we're using a generative model here, we need to be a bit creative with the prompt we provide it to minimize any hallucination or similar unwanted results. For this reason, I've tried to be a bit creative with the `PromptBuilder` template and give some examples of _how_ to construct a summary. However, this still sometimes produces odd results.
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If you try to run it yourself and find ways to make this app better, please feel free to create an issue/PR 🙌
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## To learn more about the PromptBuilder
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As of Haystack 2.0-Beta onwards, you can create prompt templates with Jinja. Check out guide on creating prompts [here](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder)
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## Installation and Running
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To run the bare application which does _nothing_:
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app.py
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if st.session_state.result:
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voice = st.session_state.result
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st.write(voice[
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if st.session_state.result:
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voice = st.session_state.result
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st.write(voice[0])
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requirements.txt
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farm-haystack==1.20.0
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streamlit==1.21.0
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markdown
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st-annotated-text
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python-dotenv
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mastodon-fetcher-haystack
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haystack-ai==2.0.0b4
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streamlit==1.21.0
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markdown
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st-annotated-text
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python-dotenv
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mastodon-fetcher-haystack
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utils/haystack.py
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import streamlit as st
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from mastodon_fetcher_haystack.mastodon_fetcher import MastodonFetcher
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from haystack import Pipeline
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from haystack.
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def start_haystack(openai_key):
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#Use this function to contruct a pipeline
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fetcher = MastodonFetcher()
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mastodon_template =
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st.session_state["haystack_started"] = True
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mastodon_pipeline = Pipeline()
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mastodon_pipeline.
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mastodon_pipeline.
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return mastodon_pipeline
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@st.cache_data(show_spinner=True)
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def query(username, _pipeline):
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try:
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except Exception as e:
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result = ["Please make sure you are providing a correct, public Mastodon account"]
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return result
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import streamlit as st
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from mastodon_fetcher_haystack.mastodon_fetcher import MastodonFetcher
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from haystack import Pipeline
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from haystack.components.generators import OpenAIGenerator
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from haystack.components.builders import PromptBuilder
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def start_haystack(openai_key):
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#Use this function to contruct a pipeline
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fetcher = MastodonFetcher()
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mastodon_template = """You will be given a post stream belonging to a specific Mastodon profile. Answer with a summary of what they've lately been posting about and in what languages.
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You may go into some detail about what topics they tend to like postint about. Please also mention their overall tone, for example: positive,
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negative, political, sarcastic or something else.
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Examples:
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Post stream: [@deepset_ai](https://mastodon.social/@deepset_ai): Come join our Haystack server for our first Discord event tomorrow, a deepset AMA session with @rusic_milos @malte_pietsch…
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[@deepset_ai](https://mastodon.social/@deepset_ai): Join us for a chat! On Thursday 25th we are hosting a 'deepset - Ask Me Anything' session on our brand new Discord. Come…
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[@deepset_ai](https://mastodon.social/@deepset_ai): Curious about how you can use @OpenAI GPT3 in a Haystack pipeline? This week we released Haystack 1.7 with which we introdu…
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[@deepset_ai](https://mastodon.social/@deepset_ai): So many updates from @deepset_ai today!
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Summary: This user has lately been reposting posts from @deepset_ai. The topics of the posts have been around the Haystack community, NLP and GPT. They've
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been posting in English, and have had a positive, informative tone.
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Post stream: I've directed my team to set sharper rules on how we deal with unidentified objects.\n\nWe will inventory, improve ca…
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the incursion by China’s high-altitude balloon, we enhanced radar to pick up slower objects.\n \nBy doing so, w…
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I gave an update on the United States’ response to recent aerial objects.
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Summary: This user has lately been posting about having sharper rules to deal with unidentified objects and an incursuin by China's high-altitude
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baloon. Their pots have mostly been neutral but determined in tone. They mostly post in English.
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Post stream: {{ documents }}
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Summary:
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"""
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prompt_builder = PromptBuilder(template=mastodon_template)
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llm = OpenAIGenerator(model_name="gpt-4", api_key=openai_key)
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st.session_state["haystack_started"] = True
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mastodon_pipeline = Pipeline()
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mastodon_pipeline.add_component("fetcher", fetcher)
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mastodon_pipeline.add_component("prompt_builder", prompt_builder)
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mastodon_pipeline.add_component("llm", llm)
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mastodon_pipeline.connect("fetcher.documents", "prompt_builder.documents")
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mastodon_pipeline.connect("prompt_builder.prompt", "llm.prompt")
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return mastodon_pipeline
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@st.cache_data(show_spinner=True)
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def query(username, _pipeline):
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try:
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replies = _pipeline.run(data={"fetcher": {"username": username,
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"last_k_posts": 20}})
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result = replies['llm']['replies']
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except Exception as e:
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result = ["Please make sure you are providing a correct, public Mastodon account"]
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return result
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utils/ui.py
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st.markdown("---")
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st.markdown(
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"## How this works\n"
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"This app was built with [Haystack](https://haystack.deepset.ai) using the"
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" [`
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" The source code is also on [GitHub](https://github.com/TuanaCelik/should-i-follow)"
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" with instructions to run locally.\n"
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"You can see how the `PromptNode` was set up [here](https://github.com/TuanaCelik/should-i-follow/blob/main/utils/haystack.py)")
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st.markdown("---")
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st.markdown(
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"## How this works\n"
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"This app was built with [Haystack 2.0-Beta](https://haystack.deepset.ai) using the"
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" [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) and [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder).\n\n"
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" The source code is also on [GitHub](https://github.com/TuanaCelik/should-i-follow)"
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" with instructions to run locally.\n"
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"You can see how the `PromptNode` was set up [here](https://github.com/TuanaCelik/should-i-follow/blob/main/utils/haystack.py)")
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