Update README.md

README.md

@@ -1,40 +1,8 @@
-# RoboAdvisor
-
-## What it does and how it works
-1) User gives a stock ticker symbol
-2) The bot queries the Finnhub API and searches for articles, news, Tweets, etc about this company over the last 7 days and downloads them
-3) It then converts the documents into smaller chunks, and uses LLM vector embeddings to convert the documents into a vector index DB, for easy querying
-4) When the user asks (prompts) a question, a vector embedding of the query/prompt is calculated, and a similarity search of this prompt vector is performed against the vector index DB
-5) The top 'k' chunks are retrieved according to the vector similarity search (in this particular case, I am using the FAISS algorithm to perform the similarity search)
-6) The bot then queries the OpenAI GPT-3.5-Turbo API to query on those retrieved chunks, and returns a response.
-
-In short, given a stock ticker symbol, this app uses GPT-3.5 to give investment outlook about it by reading articles, tweets and news about that company
-
-## Steps to install Ta-Lib in Linux
-
-```
-wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
-tar -xzf ta-lib-0.4.0-src.tar.gz
-cd ta-lib/
-./configure --prefix=/usr
-make
-sudo make install
-pip install ta-lib
-```
-Run `pip install -r requirements.txt` only after install Ta-Lib correctly.
-
-## Steps for Kuberenetes deplolyment is GCP
-
-1. docker build -t robo_advisor .
-2. docker tag robo_advisor gcr.io/asom-barta-qna-bot/robo_advisor
-3. docker push gcr.io/asom-barta-qna-bot/robo_advisor
-4. Go to "container registry" and verify that the docker image is present
-5. Go to 'Kubernetes Engine' in the Google Cloud Console and create a new Kubernetes cluster in Autopilot mode. Select a location in Asia. Wait for the k8s cluster to be created.
-6. Once the cluster is created, create a deployment. Select the docker image from container registry, give a suitable name to the deployment.
-7. Click on "Expose deployment as a new service", and set the port as 7860 (since the default port used by the Gradio app is 7860), and deploy.
-
-It may appear like this at first:
-
-![image](https://github.com/PrashantSaikia/RoboAdvisor/assets/39755678/164ef861-8689-44d5-8709-851c36f3bc8c)
-
-But it just needs some time to allocate the resources. Check back after 10-15 mins, it should be all green and ready.
+title: Robo Advisor
+emoji: 🏃
+colorFrom: gray
+colorTo: purple
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: false