Update app.py

app.py

@@ -1,23 +1,20 @@
-import os
 import streamlit as st
-from clarifai.client.model import Model
 
-# Read API key from environment variable
-api_key = os.getenv("CodestralPat")
+# Set the title of the Streamlit app
+st.title("CodeBlast Dream Catcher")
 
-# Function to get prediction from the model
-def get_model_prediction(prompt):
-    model_url = "https://clarifai.com/mistralai/completion/models/codestral-22b-instruct"
-    model = Model(url=model_url, pat=api_key)
-    model_prediction = model.predict_by_bytes(prompt.encode(), input_type="text")
-    return model_prediction.outputs[0].data.text.raw
+# Display the content
+content = """
+**CodeBlast Dream Catcher** proposes an alternative approach for searching multidimensional space for knowledge based on the following principles:
 
-# Streamlit interface
-st.title("Codestral with Clarifai")
+1. An "all possible combinations space" exists in multidimensional space where one doesn't create knowledge but discovers it.
+2. This multidimensional space is best searched with LLMs using goals, as goals carry the recipes for accomplishing them.
+3. There exists a multidimensional "all possible" goal space consisting of interconnected goals resembling a graph.
+4. This multidimensional goals space can best be searched by remapping it to the 2D Infinite Canvas proposed in the LabLab.ai Build Your Business StartUp Hackathon's "Navigating the Infinite Plane".
+5. The infinite canvas can be created using a 50256 base number system derived from the GPT-2 tokenization labels.
+6. To avoid the costly computational expense of base number conversion, hidden and unhidden states are created in the 2D infinite plane.
+7. These hidden and unhidden states correspond to the conscious and unconscious mind, proposing that the human brain uses a similar mechanism to avoid the heavy cost of base number conversion.
+8. Thus, searching for knowledge becomes a simple mapping problem in 2D and 1D space in both hidden and unhidden states.
+"""
 
-prompt = st.text_input("Enter your prompt:", "What's the future of AI?")
-
-if st.button("Get Prediction"):
-    prediction = get_model_prediction(prompt)
-    st.write("Model Prediction:")
-    st.write(prediction)
+st.markdown(content)