Spaces:
Sleeping
Sleeping
File size: 4,008 Bytes
252ec19 d8ba00f 252ec19 be25975 252ec19 be25975 252ec19 d8ba00f 25cc5a2 d8ba00f 5b17ad4 d8ba00f 252ec19 d8ba00f 25cc5a2 d8ba00f 5b17ad4 d8ba00f 252ec19 0f7e00d 207ae76 0f7e00d 58daaac 0f7e00d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
import streamlit as st
import openai
# Initialize the OpenAI API
openai.api_key = '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' # Remember never to expose API keys in code
KNOWN_MODELS = [
# General ML models
"Neural Networks", "Decision Trees", "Support Vector Machines",
"Random Forests", "Linear Regression", "Reinforcement Learning",
"Logistic Regression", "k-Nearest Neighbors", "Naive Bayes",
"Gradient Boosting Machines", "Regularization Techniques",
"Ensemble Methods", "Time Series Analysis",
# Deep Learning models
"Deep Learning", "Convolutional Neural Networks",
"Recurrent Neural Networks", "Transformer Models",
"Generative Adversarial Networks", "Autoencoders",
"Bidirectional LSTM", "Residual Networks (ResNets)",
"Variational Autoencoders",
# Computer Vision models and techniques
"Object Detection (e.g., YOLO, SSD)", "Semantic Segmentation",
"Image Classification", "Face Recognition", "Optical Character Recognition (OCR)",
"Pose Estimation", "Style Transfer", "Image-to-Image Translation",
"Image Generation", "Capsule Networks",
# NLP models and techniques
"BERT", "GPT", "ELMo", "T5", "Word2Vec", "Doc2Vec",
"Topic Modeling", "Sentiment Analysis", "Text Classification",
"Machine Translation", "Speech Recognition", "Sequence-to-Sequence Models",
"Attention Mechanisms", "Named Entity Recognition", "Text Summarization"
]
def recommend_ai_model_via_gpt(description):
messages = [
{"role": "user", "content": f"Given the application described as: '{description}', which AI model would be most suitable?"}
]
try:
response = openai.ChatCompletion.create(
model="gpt-4",
messages=messages
)
recommendation = response['choices'][0]['message']['content'].strip()
return recommendation
except openai.error.OpenAIError as e:
return f"Error: {e}"
def explain_recommendation(model_name):
messages = [
{"role": "user", "content": f"Why would {model_name} be a suitable choice for the application?"}
]
try:
response = openai.ChatCompletion.create(
model="gpt-4",
messages=messages
)
explanation = response['choices'][0]['message']['content'].strip()
return explanation
except openai.error.OpenAIError as e:
return f"Error: {e}"
# Streamlit UI
st.title('AI Model Recommender')
description = st.text_area("Describe your application:", "")
if "rec_model_pressed" not in st.session_state:
st.session_state.rec_model_pressed = False
if "feedback_submitted" not in st.session_state:
st.session_state.feedback_submitted = False
if st.button("Recommend AI Model"):
st.session_state.rec_model_pressed = True
if st.session_state.rec_model_pressed:
if description:
recommended_model = recommend_ai_model_via_gpt(description)
# Validate recommended model
# Commenting out model validation for the example
# if recommended_model not in KNOWN_MODELS:
# st.warning("The recommendation is ambiguous. Please refine your description or consult an expert.")
# else:
st.subheader(f"Recommended AI Model: {recommended_model}")
explanation = explain_recommendation(recommended_model)
st.write("Reason:", explanation)
# Collecting rating and feedback through Streamlit
rating = st.slider("Rate the explanation from 1 (worst) to 5 (best):", 1, 5)
feedback = st.text_input("Any additional feedback?")
if st.button("Submit Feedback"):
st.session_state.feedback_submitted = True
if st.session_state.feedback_submitted:
st.success("Thank you for your feedback!")
st.success("Contact [email protected] or call (857) 600-0180 to learn how we can fine-tune and host this app for you.")
else:
st.warning("Please provide a description.") |