app.py

import streamlit as st
import openai
import os

# Securely fetch the OpenAI API key
#try:
openai.api_key = ('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')
#except openai.APIError as e:
    # Log the error for debugging purposes
    #st.error("An error occurred while communicating with the OpenAI API.")
    # Optionally, log more detailed information for debugging
    #print("Error details:", e)
    # You might also want to implement additional logic here, like retrying the request,
    # sending a notification to an administrator, or providing a default response.


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()

    #except openai.APIError as e:
    #    return f"Error: {e}"
    #except openai.RateLimitError as e:
    #    return f"Rate limit exceeded: {e}"
    #except openai.APIConnectionError as e:
    #    return f"Connection error: {e}"


def explain_recommendation(model_name):
    messages = [
        {"role": "user", "content": f"Why would {model_name} be a suitable choice for the application?"}
    ]
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=messages
    )
    return response['choices'][0]['message']['content'].strip()
    #except openai.APIError as e:
    #    return f"Error: {e}"
    #except openai.RateLimitError as e:
    #    return f"Rate limit exceeded: {e}"
    #except openai.APIConnectionError as e:
    #    return f"Connection error: {e}"

# Streamlit UI
st.image("./A8title2.png")
st.title('Find the best AI stack for your app')

description = st.text_area("Describe your application:")
dataset_description = st.text_area("Describe the dataset you want to use for fine-tuning your model:")

recommendation_type = st.radio("What type of recommendation are you looking for?", ["Recommend Open-Source Model", "Recommend API Service"])

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 and dataset_description:
        combined_query = f"{description} Dataset: {dataset_description}"
        recommended_model = recommend_ai_model_via_gpt(combined_query)
        st.subheader(f"Recommended: {recommended_model}")
        explanation = explain_recommendation(recommended_model)
        st.write("Reason:", 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"):
            st.session_state.feedback_submitted = True

        if st.session_state.feedback_submitted:
            st.success("Thank you for your feedback!")

else:
    st.warning("Please provide a description and dataset details.")