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import streamlit as st
import openai
import os
# Securely fetch the OpenAI API key
openai.api_key = os.getenv("OPENAI_API_KEY")
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": description}]
response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
return response['choices'][0]['message']['content'].strip()
def explain_recommendation(model_name):
messages = [{"role": "user", "content": f"Why is {model_name} the best choice for my application?"}]
response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
return response['choices'][0]['message']['content'].strip()
# Streamlit UI
st.image("./A8title.png")
st.title('Find the best model for your GenAI App')
st.write("")
st.markdown("<h4 style='font-size:20px;'>Outline Your Application's Functionality:</h4>", unsafe_allow_html=True)
description = "Recommend from models on HuggingFace.com" + st.text_area("", key="app_description")
# Hardcoded 'Next' button that is always visible
if st.button("Next step: Dataset", key="next_to_dataset"):
st.session_state.show_dataset_description = True
if 'show_dataset_description' in st.session_state and st.session_state.show_dataset_description:
st.markdown("<h4 style='font-size:20px;'>Describe training dataset you will use:</h4>", unsafe_allow_html=True)
dataset_description = st.text_area("", key="dataset_description")
if st.button("Recommend AI Model", key="recommend_model_button"):
recommended_model = recommend_ai_model_via_gpt(description)
st.subheader(f"Recommended: {recommended_model}")
explanation = explain_recommendation(recommended_model)
st.write("Explanation:", explanation)
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", key="submit_feedback_key"):
st.session_state.feedback_submitted = True
if st.session_state.feedback_submitted:
st.success("Thank you for your feedback!") |