app.py

import gradio as gr
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
import pandas as pd
import plotly.express as px
import umap

model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

default_problems = """
I manually rename files every week
I convert PDFs to Excel
I copy data between spreadsheets
I send weekly reports manually
I merge CSV files daily
I manually download invoices
I extract tables from PDFs
I clean messy Excel sheets
I manually schedule social posts
I track expenses in spreadsheets
"""

def analyze_problems(text):

    problems = [p.strip() for p in text.split("\n") if p.strip()]

    embeddings = model.encode(problems)

    k = min(5, len(problems))
    kmeans = KMeans(n_clusters=k, random_state=0).fit(embeddings)

    reducer = umap.UMAP()
    coords = reducer.fit_transform(embeddings)

    df = pd.DataFrame({
        "problem": problems,
        "cluster": kmeans.labels_,
        "x": coords[:,0],
        "y": coords[:,1]
    })

    fig = px.scatter(
        df,
        x="x",
        y="y",
        color=df["cluster"].astype(str),
        text="problem",
        title="Problem Market Map"
    )

    cluster_summary = df.groupby("cluster")["problem"].apply(list).to_dict()

    summary = ""

    for c, items in cluster_summary.items():
        summary += f"\nCluster {c}\n"
        for i in items:
            summary += f"- {i}\n"

    return summary, fig


demo = gr.Interface(
    fn=analyze_problems,
    inputs=gr.Textbox(value=default_problems, lines=15, label="Problem Signals"),
    outputs=[
        gr.Textbox(label="Problem Clusters"),
        gr.Plot(label="Problem Market Map")
    ],
    title="Problem Discovery Engine Demo",
)

demo.launch()