Update app.py

app.py

@@ -1,2 +1,106 @@
-from transformers import pipeline
-print(pipeline('sentiment-analysis')('Test'))
+import os
+import streamlit as st
+from llm_groq import generate_post, transform_post, generate_hooks, DEFAULT_MODEL
+from prompts import build_post_prompt, transform_instruction
+from data_utils import load_posts, extract_keywords, dedupe_sentences, strip_labels
+from ui_components import preset_picker, refinement_bar
+
+st.set_page_config(page_title="Pro LinkedIn Post Generator — Groq", layout="centered")
+st.title("Pro LinkedIn Post Generator (Groq)")
+
+# Sidebar controls
+with st.sidebar:
+    st.subheader("Groq & Decoding")
+    model = st.selectbox("Model", [DEFAULT_MODEL, "llama-3.1-8b-instant", "mixtral-8x7b-32768"], index=0)
+    temperature = st.slider("Temperature", 0.1, 1.2, 0.6, 0.05)
+    top_p = st.slider("Top‑p", 0.1, 1.0, 0.9, 0.05)
+    target_len = st.slider("Length (words)", 60, 320, 160, 10)
+    st.markdown("Set GROQ_API_KEY in Space → Settings → Variables & Secrets.")
+
+# Presets to speed up intent
+preset = preset_picker()
+
+# Core inputs
+st.markdown("### Compose")
+topic = st.text_input("Topic", value="AI agent playbooks for startup ops")
+purpose = st.selectbox("Purpose", ["awareness","lead-gen","hiring","product launch","opinion","lesson learned"], index=0 if not preset else ["awareness","lead-gen","hiring","product launch","opinion","lesson learned"].index(preset.get("purpose","awareness")))
+audience = st.text_input("Audience", value="SaaS founders in early stage")
+tone = st.selectbox("Tone", ["Professional","Friendly","Contrarian","Technical","Inspirational"], index=0 if not preset else ["Professional","Friendly","Contrarian","Technical","Inspirational"].index(preset.get("tone","Professional")))
+language = st.selectbox("Language", ["English","Urdu","Arabic","French","Spanish"], index=0)
+
+# Evidence & style
+st.markdown("### Evidence & Style")
+evidence = st.text_area("Evidence (metric/anecdote/quote)", value="")
+style_text = st.text_area("Style cues (max 4, one per line)", value="Short punchy hooks\nActionable bullets\nOne metric per paragraph\nQuestion-led CTA")
+
+# Dataset upload (optional)
+st.markdown("### Optional Dataset")
+uploaded = st.file_uploader("Upload CSV/JSON of past posts (must include 'text')", type=["csv","json"])
+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}")
+
+# Hook lab (lightweight)
+st.markdown("### Hook Lab")
+hook_btn = st.button("Suggest 5 hooks")
+chosen_hook = st.text_input("Chosen opening line (optional)")
+if hook_btn:
+    try:
+        raw_hooks = generate_hooks(topic, audience, tone, 5, model, temperature, top_p, 200)
+        st.code(raw_hooks)
+    except Exception as e:
+        st.error(f"Hook generation failed: {e}")
+
+# Clarifier (simple)
+st.markdown("### Clarifier")
+outcome = st.text_input("Desired outcome (e.g., 10 demo requests this week)", value="")
+extra_note = st.text_input("One concrete detail to include (e.g., 'onboarding 14→3 days')", value="")
+clarifier_notes = "\n".join([f"Outcome: {outcome}" if outcome else "", f"Detail: {extra_note}" if extra_note else ""]).strip()
+
+if st.button("Generate Post"):
+    try:
+        style_cues = [s.strip() for s in style_text.splitlines() if s.strip()][:4]
+        prompt = build_post_prompt(
+            topic=topic,
+            language=language,
+            tone=tone,
+            target_len=target_len,
+            purpose=purpose,
+            audience=audience,
+            evidence=evidence,
+            keywords=keywords,
+            style_cues=style_cues,
+            clarifier_notes=clarifier_notes,
+            chosen_hook=chosen_hook
+        )
+        max_tokens = max(200, min(1200, int(target_len*1.6)+120))
+        raw = generate_post(prompt, model, temperature, top_p, max_tokens)
+        post = dedupe_sentences(strip_labels(raw))
+        st.session_state["post"] = post
+        st.success("Post generated.")
+        st.write(post)
+        st.download_button("Download (.txt)", post, file_name="linkedin_post.txt")
+        st.code(prompt, language="markdown")
+    except Exception as e:
+        st.error(f"Generation failed: {e}")
+
+# Refinement bar
+if "post" in st.session_state and st.session_state["post"]:
+    st.markdown("---")
+    st.subheader("Refine")
+    actions = refinement_bar()
+    for kind, pressed in actions.items():
+        if pressed:
+            try:
+                instr = transform_instruction(kind)
+                raw = transform_post(instr, st.session_state["post"], model, temperature, top_p, 500)
+                st.session_state["post"] = dedupe_sentences(strip_labels(raw))
+            except Exception as e:
+                st.error(f"Refinement failed: {e}")
+    if st.session_state["post"]:
+        st.write(st.session_state["post"])