import os import re import streamlit as st from llm_groq import generate_post, transform_post, generate_hooks, DEFAULT_MODEL from prompts import build_quick_prompt, build_post_prompt, transform_instruction from data_utils import load_posts, extract_keywords, dedupe_sentences, strip_labels from ui_components import quick_controls, pro_controls st.set_page_config(page_title="LinkedIn Post Generator — Groq", layout="centered") st.title("LinkedIn Post Generator — Quick & Pro ") # Sidebar with st.sidebar: st.subheader("Groq & Decoding") model = st.selectbox("Model", [DEFAULT_MODEL, "llama-3.1-8b-instant", "mixtral-8x7b-32768"], index=0, key="sb_model") temperature = st.slider("Temperature", 0.1, 1.2, 0.6, 0.05, key="sb_temp") top_p = st.slider("Top‑p", 0.1, 1.0, 0.9, 0.05, key="sb_topp") st.markdown("Set GROQ_API_KEY in Space → Settings → Variables & Secrets.") tabs = st.tabs(["Quick Draft", "Pro Mode", "History"]) if "history" not in st.session_state: st.session_state.history = [] def quick_quality_fix(text, want_hashtags=True, allow_emoji=True): lines = [l for l in text.strip().splitlines() if l.strip()] if len(lines) < 4 or len(lines) > 7: return None if not allow_emoji: text = re.sub(r"[^\w\s#.,:;%&()\-\+\[\]{}'\"/]", "", text) tags = re.findall(r"#\w+", text) if not want_hashtags and tags: for t in tags: text = text.replace(t, "") if want_hashtags and len(tags) > 2: for t in tags[2:]: text = text.replace(t, "") return text.strip() # Quick Draft with tabs[0]: idea, tone, words, variations, include_emoji, add_hashtags, language = quick_controls() if st.button("Generate", key="qd_generate"): if not os.getenv("GROQ_API_KEY"): st.error("GROQ_API_KEY missing.") elif not idea.strip(): st.warning("Enter your idea.") else: prompt = build_quick_prompt(idea, tone, words, include_emoji, add_hashtags, language) posts = [] with st.spinner("Generating…"): try: max_tokens = max(200, min(1200, int(words*1.6)+120)) for _ in range(variations): raw = generate_post(prompt, model, temperature, top_p, max_tokens) clean = dedupe_sentences(strip_labels(raw)) fixed = quick_quality_fix(clean, want_hashtags=add_hashtags, allow_emoji=include_emoji) if fixed is None: corrective = ( prompt + f"\n\nRegenerate a full LinkedIn post around {words} words total, " "structured in 4–6 short paragraphs (each 2–3 lines). " "Keep it scannable, professional, and engaging. " "include one concrete metric or date, " f"{'max 5 emoji' if include_emoji else 'no emojis'}, " f"{'1–2 niche hashtags at the end' if add_hashtags else 'no hashtags'}." ) raw2 = generate_post(corrective, model, temperature, top_p, max_tokens) clean2 = dedupe_sentences(strip_labels(raw2)) fixed = quick_quality_fix(clean2, want_hashtags=add_hashtags, allow_emoji=include_emoji) or clean2 posts.append(fixed) except Exception as e: st.error(f"Generation failed: {e}") posts = [] for i, p in enumerate(posts, start=1): st.markdown(f"#### Post {i}") st.write(p) st.download_button(f"Download Post {i}", p, file_name=f"post_{i}.txt", key=f"qd_dl_{i}") if posts: st.session_state.history.append({"mode":"quick","idea":idea,"tone":tone,"words":words,"posts":posts}) # Pro Mode with tabs[1]: st.markdown("Upload CSV/JSON of past posts (must include 'text') to auto-extract keywords (optional).") uploaded = st.file_uploader("Upload dataset", type=["csv","json"], key="pro_upload") defaults = {"topic":"AI agent playbooks for startup ops","audience":"SaaS founders in early stage"} topic, purpose, audience, tone2, language2, evidence, style_text = pro_controls(defaults) keywords = [] if uploaded is not None: try: df = load_posts(uploaded) keywords = extract_keywords(topic, df) st.success(f"Loaded {len(df)} posts. Extracted keywords: {', '.join(keywords[:8]) or '—'}") except Exception as e: st.error(f"Dataset error: {e}") if st.button("Suggest 5 hooks", key="pro_hooks_btn"): try: hooks = generate_hooks(topic, audience, tone2, 5, model, temperature, top_p, 200) st.code(hooks) except Exception as e: st.error(f"Hook generation failed: {e}") chosen_hook = st.text_input("Chosen opening line (optional)", key="pro_chosen_hook") outcome = st.text_input("Desired outcome (e.g., 10 demo requests this week)", value="", key="pro_outcome") extra_detail = st.text_input("One concrete detail (e.g., 'onboarding 14→3 days')", value="", key="pro_detail") clarifier_notes = "\n".join([f"Outcome: {outcome}" if outcome else "", f"Detail: {extra_detail}" if extra_detail else ""]).strip() style_cues = [s.strip() for s in style_text.splitlines() if s.strip()][:4] if st.button("Generate Post (Pro)", key="pro_generate"): if not os.getenv("GROQ_API_KEY"): st.error("GROQ_API_KEY missing.") else: try: prompt = build_post_prompt(topic, language2, tone2, 160, purpose, audience, evidence, keywords, style_cues, clarifier_notes, chosen_hook) raw = generate_post(prompt, model, temperature, top_p, 800) post = dedupe_sentences(strip_labels(raw)) st.success("Post") st.write(post) st.download_button("Download (.txt)", post, file_name="linkedin_post.txt", key="pro_download") st.session_state.history.append({"mode":"pro","topic":topic,"audience":audience,"post":post}) except Exception as e: st.error(f"Generation failed: {e}") # Refinements col1,col2,col3,col4,col5 = st.columns(5) def refine(kind): if st.session_state.get("history") and st.session_state.history[-1].get("post"): try: instr = transform_instruction(kind) raw = transform_post(instr, st.session_state.history[-1]["post"], model, temperature, top_p, 500) st.session_state.history[-1]["post"] = dedupe_sentences(strip_labels(raw)) except Exception as e: st.error(f"Refinement failed: {e}") if col1.button("Shorter", key="pro_shorter"): refine("shorter") if col2.button("Punchier hook", key="pro_punchy"): refine("punchier") if col3.button("Add data point", key="pro_adddata"): refine("add_data") if col4.button("No emojis", key="pro_noemoji"): refine("less_emoji") if col5.button("Add hashtags", key="pro_addtags"): refine("add_tags") if st.session_state.get("history") and st.session_state.history[-1].get("post"): st.write(st.session_state.history[-1]["post"]) # History with tabs[2]: if not st.session_state.history: st.info("No saved drafts yet.") else: for i, item in enumerate(reversed(st.session_state.history), start=1): st.markdown(f"#### Draft {i} ({item.get('mode')})") if "posts" in item: for j, p in enumerate(item["posts"], start=1): st.markdown(f"Post {j}") st.write(p) else: st.write(item.get("post",""))