update requirements.txt

README 2.md

@@ -20,86 +20,29 @@ All code was written with the help of <a href="https://codegpt.co">Code GPT</a>
 - Embedding texts segments with Langchain and OpenAI (**text-embedding-ada-002**)
 - Chat with the file using **streamlit-chat** and LangChain QA with source and (**text-davinci-003**)
 
-# Example
-For this example we are going to use this video from The PyCoach
-https://youtu.be/lKO3qDLCAnk
-
-Add the video URL and then click Start Analysis
-![Youtube](https://user-images.githubusercontent.com/6216945/217701635-7c386ca7-c802-4f56-8148-dcce57555b5a.gif)
-
-## Pytube and OpenAI Whisper
-The video will be downloaded with pytube and then OpenAI Whisper will take care of transcribing and segmenting the video.
-![Pyyube Whisper](https://user-images.githubusercontent.com/6216945/217704219-886d0afc-4181-4797-8827-82f4fd456f4f.gif)
-
-```python
-# Get the video 
-youtube_video = YouTube(youtube_link)
-streams = youtube_video.streams.filter(only_audio=True)
-mp4_video = stream.download(filename='youtube_video.mp4')
-audio_file = open(mp4_video, 'rb')
-
-# whisper load base model
-model = whisper.load_model('base')
-
-# Whisper transcription
-output = model.transcribe("youtube_video.mp4")
-```
-
-## Embedding with "text-embedding-ada-002"
-We obtain the vectors with **text-embedding-ada-002** of each segment delivered by whisper
-![Embedding](https://user-images.githubusercontent.com/6216945/217705008-180285d7-6bce-40c3-8601-576cc2f38171.gif)
-
-```python
-# Embeddings
-segments = output['segments']
-for segment in segments:
-    openai.api_key = user_secret
-    response = openai.Embedding.create(
-        input= segment["text"].strip(),
-        model="text-embedding-ada-002"
-    )
-    embeddings = response['data'][0]['embedding']
-    meta = {
-        "text": segment["text"].strip(),
-        "start": segment['start'],
-        "end": segment['end'],
-        "embedding": embeddings
-    }
-    data.append(meta)
-pd.DataFrame(data).to_csv('word_embeddings.csv') 
-```
-## OpenAI GPT-3
-We make a question to the vectorized text, we do the search of the context and then we send the prompt with the context to the model "text-davinci-003"
-
-![Question1](https://user-images.githubusercontent.com/6216945/217708086-b89dce2e-e3e2-47a7-b7dd-77e402d818cb.gif)
-
-We can even ask direct questions about what happened in the video. For example, here we ask about how long the exercise with Numpy that Pycoach did in the video took.
-
-![Question2](https://user-images.githubusercontent.com/6216945/217708485-df1edef3-d5f1-4b4a-a5c9-d08f31c80be4.gif)
-
 # Running Locally
 
 1. Clone the repository
 
 ```bash
-git clone https://github.com/davila7/youtube-gpt
-cd youtube-gpt
+git clone https://github.com/davila7/file-gpt
+cd file-gpt
 ```
 2. Install dependencies
 
 These dependencies are required to install with the requirements.txt file:
 
-* streamlit 
-* streamlit_chat 
-* matplotlib 
-* plotly 
-* scipy 
-* sklearn 
-* pandas 
-* numpy 
-* git+https://github.com/openai/whisper.git 
-* pytube 
-* openai-whisper
+* openai
+* pypdf
+* scikit-learn
+* numpy
+* tiktoken
+* docx2txt
+* langchain
+* pydantic
+* typing
+* faiss-cpu
+* streamlit_chat
 
 ```bash
 pip install -r requirements.txt
@@ -109,9 +52,3 @@ pip install -r requirements.txt
 ```bash
 streamlit run app.py
 ```
-
-## Upcoming Features 🚀
-
-- Semantic search with embedding
-- Chart with emotional analysis
-- Connect with Pinecone

requirements.txt

@@ -7,4 +7,5 @@ docx2txt
 langchain
 pydantic
 typing
-faiss-cpu
+faiss-cpu
+streamlit_chat