Create app.py

app.py

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+# app.py — super-minimal ProdBot (Chat + Backlog only) for Hugging Face Spaces
+import gradio as gr
+import pandas as pd
+from rapidfuzz import fuzz
+import json, uuid, datetime as dt, re
+
+STATE = {"features": [], "feedback": []}
+
+def now_iso(): return dt.datetime.utcnow().isoformat() + "Z"
+def mk_id(prefix="FB"): return f"{prefix}{uuid.uuid4().hex[:8]}"
+
+EMAIL_RE = re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}")
+PHONE_RE = re.compile(r"\+?\d[\d\s\-()]{7,}\d")
+IP_RE    = re.compile(r"\b(?:\d{1,3}\.){3}\d{1,3}\b")
+def redact(text: str) -> str:
+    text = EMAIL_RE.sub("[REDACTED_EMAIL]", text)
+    text = PHONE_RE.sub("[REDACTED_PHONE]", text)
+    text = IP_RE.sub("[REDACTED_IP]", text)
+    return text
+
+POS_WORDS = {"love","great","awesome","amazing","nice","helpful","good"}
+NEG_WORDS = {"slow","bad","hate","broken","fail","fails","error","crash","confusing","hard","bug"}
+def tiny_sentiment(text: str):
+    t = text.lower()
+    pos = sum(w in t for w in POS_WORDS)
+    neg = sum(w in t for w in NEG_WORDS)
+    score = (pos - neg)
+    label = "positive" if score>0 else "negative" if score<0 else "neutral"
+    score = max(-1, min(1, score/3))
+    return label, round(score, 2)
+
+def seed_demo():
+    if STATE["features"]: return
+    now = now_iso()
+    STATE["features"] = [
+        {"feature_id":"F101","title":"Invoice PDF export","description":"Export invoices as PDF",
+         "tags":["billing","export"],"status":"planned",
+         "demand_signals":{"feedback_count":14,"unique_users":11,"segments":{"pro":6,"enterprise":5}},
+         "priority_score":None,"subscribers":["finance@corp"],"updated_at":now},
+        {"feature_id":"F102","title":"Usage-based billing","description":"Metered billing by API calls",
+         "tags":["billing","pricing"],"status":"idea",
+         "demand_signals":{"feedback_count":8,"unique_users":8,"segments":{"enterprise":8}},
+         "priority_score":None,"subscribers":[],"updated_at":now},
+        {"feature_id":"F201","title":"Offline editing","description":"Work without internet, sync later",
+         "tags":["editor","offline","mobile"],"status":"planned",
+         "demand_signals":{"feedback_count":12,"unique_users":10,"segments":{"free":4,"pro":4,"enterprise":2}},
+         "priority_score":None,"subscribers":["pm_editor@corp"],"updated_at":now},
+        {"feature_id":"F202","title":"Comment threading","description":"Nested comments in editor",
+         "tags":["editor","collaboration","ux"],"status":"in_development",
+         "demand_signals":{"feedback_count":18,"unique_users":15,"segments":{"free":5,"pro":6,"enterprise":4}},
+         "priority_score":None,"subscribers":[],"updated_at":now},
+        {"feature_id":"F301","title":"Slack integration","description":"Notify Slack channels on changes",
+         "tags":["collaboration","integration"],"status":"beta",
+         "demand_signals":{"feedback_count":9,"unique_users":7,"segments":{"pro":5,"enterprise":2}},
+         "priority_score":None,"subscribers":["cs_team@corp"],"updated_at":now},
+        {"feature_id":"F302","title":"Role-based access control","description":"Viewer/Editor/Admin roles",
+         "tags":["collaboration","security"],"status":"planned",
+         "demand_signals":{"feedback_count":20,"unique_users":16,"segments":{"enterprise":16}},
+         "priority_score":None,"subscribers":["security@corp"],"updated_at":now}
+    ]
+    samples = [
+        ("u1","in_app","web",None,"Please add PDF invoice export."),
+        ("u2","email","web","1.2.0","Our finance team needs usage-based billing for API customers."),
+        ("u3","in_app","ios","3.0","I want to edit docs offline during flights."),
+        ("u4","survey","web",None,"Comment threads would make discussions much easier."),
+        ("u5","chat","web",None,"Slack integration is great but needs per-channel settings."),
+        ("u6","email","web","1.3.0","We require role-based access control for compliance."),
+        ("u7","in_app","android","2.5","Offline editing would be a lifesaver."),
+        ("u8","cs_transcript","web",None,"Customer asked about usage-based billing again."),
+        ("u9","in_app","web",None,"PDF invoices still missing; blocker for accounting."),
+        ("u10","in_app","desktop","2.2","Need nested comments inside editor like Google Docs.")
+    ]
+    for s in samples:
+        STATE["feedback"].append({
+            "id": mk_id("FB"), "user_id": s[0], "channel": s[1], "product_area": None,
+            "platform": s[2], "version": s[3], "text": redact(s[4]),
+            "sentiment_label": tiny_sentiment(s[4])[0],
+            "sentiment_score": tiny_sentiment(s[4])[1],
+            "language": None, "duplicates_of": None, "linked_feature_ids": [],
+            "created_at": now_iso()
+        })
+
+def simple_classify(text: str):
+    t = text.lower()
+    if any(k in t for k in ["fail","error","crash","broken","bug"]): label="bug"
+    elif any(k in t for k in ["feature","add","please","need","would like","would love","request"]): label="feature_request"
+    elif any(k in t for k in ["how do i","help","docs","documentation","guide"]): label="support"
+    else: label="feedback"
+    return {"label": label, "confidence": 0.7}
+
+def fuzzy_link(text: str):
+    scores=[]
+    for f in STATE["features"]:
+        blob = f"{f['title']} {f.get('description','')} {' '.join(f.get('tags',[]))}"
+        try:
+            from rapidfuzz import fuzz as _fuzz
+            sc = max(_fuzz.token_sort_ratio(text, blob), _fuzz.partial_ratio(text, blob))/100.0
+        except Exception:
+            sc = 0.0
+        scores.append((f["feature_id"], sc))
+    scores.sort(key=lambda x: x[1], reverse=True)
+    linked = [fid for fid, sc in scores if sc >= 0.60][:3]
+    confs = {fid: round(sc,2) for fid, sc in scores[:3]}
+    return linked, confs
+
+def chat_fn(history, message):
+    message = message.strip()
+    if not message: return history, ""
+    red = redact(message)
+    cls = simple_classify(red)
+    s_label, s_score = tiny_sentiment(red)
+    fb = {"id": mk_id("FB"), "user_id":"anon", "channel":"chat","product_area":None,
+          "platform":"unknown","version":None,"text":red,"sentiment_label":s_label,
+          "sentiment_score":s_score,"language":None,"duplicates_of":None,
+          "linked_feature_ids":[],"created_at": now_iso()}
+    STATE["feedback"].append(fb)
+    linked, confs = fuzzy_link(red); fb["linked_feature_ids"] = linked
+    if linked:
+        titles = [next(f["title"] for f in STATE["features"] if f["feature_id"]==fid) for fid in linked]
+        reply = f"Thanks! I linked your message to: {', '.join(titles)}."
+    else:
+        reply = "Thanks! I couldn't confidently link this. File as a new idea?"
+    reply += f"\n\n[rationale] label={cls['label']} ({cls['confidence']}); sentiment={s_label}({s_score}); link_conf={confs}"
+    return (history or []) + [(message, reply)], ""
+
+def tables(filter_text):
+    feats = STATE["features"]; fbs = STATE["feedback"]
+    if filter_text:
+        q = filter_text.lower()
+        feats = [f for f in feats if q in f["title"].lower() or any(q in t.lower() for t in f.get("tags",[]))]
+        fbs  = [x for x in fbs if q in x["text"].lower()]
+    return pd.DataFrame(feats), pd.DataFrame(fbs)
+
+with gr.Blocks(title="ProdBot (Minimal)") as demo:
+    gr.Markdown("# ProdBot (Minimal)\nCollect feedback ➜ auto-link to features.")
+    with gr.Tabs():
+        with gr.Tab("Chat"):
+            chat = gr.Chatbot(height=360)  # tuple mode (avoids 'metadata is required')
+            inp = gr.Textbox(placeholder="Share feedback (e.g., 'Please add PDF invoice export')", label="Message")
+            def on_submit(h, m): return chat_fn(h or [], m)
+            inp.submit(on_submit, [chat, inp], [chat, inp])
+        with gr.Tab("Backlog"):
+            filt = gr.Textbox(placeholder="Filter by title/tag/text, e.g., 'billing'", label="Filter")
+            feat_df = gr.Dataframe(label="Features", interactive=False, wrap=True)
+            fb_df = gr.Dataframe(label="Feedback", interactive=False, wrap=True)
+            refresh = gr.Button("Refresh tables")
+            refresh.click(tables, [filt], [feat_df, fb_df])
+            filt.submit(tables, [filt], [feat_df, fb_df])
+
+    def _on_load():
+        seed_demo()
+        df1, df2 = tables("")
+        return df1, df2
+    demo.load(_on_load, None, [feat_df, fb_df])
+
+if __name__ == "__main__":
+    seed_demo()
+    demo.launch()