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Update requirements.txt
Browse files- requirements.txt +25 -26
requirements.txt
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# requirements.txt
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streamlit
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Pillow
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plotly
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opencv-python
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moviepy
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python-dotenv
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pytz
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requests
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openai
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beautifulsoup4
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PyPDF2
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textract
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gradio-client
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huggingface-hub
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audio-recorder-streamlit
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xmltodict
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mistune
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python-docx
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faiss-cpu
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scikit-learn
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langchain
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tiktoken
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anthropic
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streamlit-marquee>=0.0.2 # Scroll text - streamlit_marquee(text), set_speed(), set_style()
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# requirements.txt
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streamlit # Core web app - st.write("Hello"), st.button("Click"), st.selectbox("Choose", opts)
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Pillow # Image tools - Image.open("pic.jpg"), img.resize((100,100)), img.save("out.png")
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plotly # Plots - px.scatter(df, x="a", y="b"), px.line(df), fig.show()
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opencv-python # Vision - cv2.imread("img.jpg"), cv2.resize(img, (w,h)), cv2.imwrite("out.jpg", img)
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moviepy # Video - VideoFileClip("vid.mp4"), clip.resize(0.5), clip.write_videofile("out.mp4")
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python-dotenv # Env vars - load_dotenv(), os.getenv("KEY"), dotenv_values()
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pytz # Time - timezone('US/Pacific'), dt.astimezone(tz), utc.localize(dt)
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requests # HTTP - requests.get(url), r.json(), r.raise_for_status()
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openai # AI API - client.chat.complete(), client.images.create(), client.audio.transcribe()
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beautifulsoup4 # HTML - BeautifulSoup(html), soup.find("div"), soup.find_all("p")
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PyPDF2 # PDF - PdfReader("doc.pdf"), reader.pages[0].extract_text(), writer.write(f)
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textract # Extract - process("doc.pdf"), process("doc.docx"), process("text.txt")
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gradio-client # Gradio - client.predict(text), interface.launch(), client.submit(inputs)
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huggingface-hub # Models - from_pretrained("model"), list_models(), push_to_hub()
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audio-recorder-streamlit # Audio - audio_recorder(), get_audio_data(), save_recording()
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xmltodict # XML - parse(xml_str), unparse(dict_obj), parse(f.read())
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mistune # Markdown - markdown(text), escape(html), renderer.render(tokens)
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python-docx # Word - Document(), doc.add_paragraph(), doc.save("out.docx")
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faiss-cpu # Vectors - IndexFlatL2(d), index.add(vecs), D,I = index.search(q,k)
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scikit-learn # ML - fit(X,y), predict(X), train_test_split(X,y)
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langchain # LLM - load_chain(), chain.run(input), memory.save_context()
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tiktoken # Tokens - encode(text), decode(tokens), num_tokens = len(tokens)
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anthropic # Claude - client.messages.create(), client.complete()
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edge-tts # TTS - communicate.run(text), save_to_file(), stream_to_speaker()
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streamlit-marquee # Scroll - streamlit_marquee(text), set_speed(), set_style()
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