app.py

import os
import gradio as gr
from huggingface_hub import InferenceClient
from retriever import find_similar_foundations
from retriever_m3 import find_similar_foundations_api
from chat import chat_with_model

# -------------------------------------------------------------------
# 1. Setup client for chatbot
# -------------------------------------------------------------------
# Use my token stored as a Space secret for inference
client_chat = InferenceClient(
    provider="featherless-ai",
    api_key=os.environ["HF_TOKEN_inf"]
)


# -------------------------------------------------------------------
# 2. Setup client for bgem3 similarity search
# -------------------------------------------------------------------
client_m3 = InferenceClient(
    provider="hf-inference", # for embeddings similarity
    api_key=os.environ["HF_TOKEN_inf"],
)

# -------------------------------------------------------------------
# 3. Foundations Retriever bge-m3 function API
# -------------------------------------------------------------------
def retrieve_foundations_m3(perspective, top_k=5):
    results = find_similar_foundations_api(perspective, client=client_m3, top_k=int(top_k))
    return [(r["Title"], r["Purpose"], f"{r['score']:.4f}") for r in results]


# -------------------------------------------------------------------
# 4. Foundations Retriever bge-en-icl function (for UI)
# -------------------------------------------------------------------
def retrieve_foundations(perspective, top_k=5):
    """
    Find foundations aligned with user-provided perspective.
    """
    results = find_similar_foundations(perspective, top_k=int(top_k))
    display_text = ""
    for i, res in enumerate(results, 1):
        display_text += f"{i}. {res['Title']} - {res['Purpose']} (Score: {res['Score']:.3f})\n"
    return display_text

# -------------------------------------------------------------------
# 5. Gradio Interface
# -------------------------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# Mistral Perspective Chatbot & Foundation Finder")

    with gr.Tab("💬 Chatbot"):
        perspective_input = gr.Textbox(
            label="Enter your philanthropic perspective (optional)",
            placeholder="e.g. Environmental philanthropist emphasizing animal protection while fostering children's education"
        )
        chatbot = gr.Chatbot(type="messages")
        msg = gr.Textbox(placeholder="Ask me anything...", show_label=False)
        state = gr.State([])  # stores conversation in messages format

        # Streaming callback from chat.py
        msg.submit(
            chat_with_model,
            [msg, state, perspective_input],
            [chatbot, state],
        )

    with gr.Tab("🔎 M3 Aligned Foundations"):
        perspective_api = gr.Textbox(label="Enter your philanthropic perspective")
        top_k_api = gr.Slider(1, 5, value=2, step=1, label="Number of results")
        output_api = gr.Dataframe(headers=["Title", "Purpose", "Score"])
        gr.Button("Find Foundations").click(fn=retrieve_foundations_m3, inputs=[perspective_api, top_k_api], outputs=output_api)

    
    with gr.Tab("🔎 FAISS ICL Aligned Foundations"):
        perspective = gr.Textbox(
            label="Enter your philanthropic perspective",
            placeholder="e.g. Environmental philanthropist emphasizing animal protection while fostering children's education"
        )
        top_k = gr.Slider(1, 5, value=2, step=1, label="Number of results")
        output = gr.Dataframe(headers=["Title", "Purpose", "similarity"], wrap=True)

        btn = gr.Button("Find Foundations")
        btn.click(fn=retrieve_foundations, inputs=[perspective, top_k], outputs=output)

demo.launch(server_name="0.0.0.0", server_port=7860)