Create llm_groq.py

llm_groq.py

@@ -0,0 +1,69 @@
+import os
+from typing import List, Dict, Optional
+
+try:
+    from groq import Groq
+except ImportError:
+    Groq = None
+
+DEFAULT_MODEL = "llama-3.3-70b-versatile"
+
+def get_client() -> "Groq":
+    api_key = os.getenv("GROQ_API_KEY")
+    if not api_key:
+        raise RuntimeError("Missing GROQ_API_KEY (set in Space → Settings → Variables & Secrets).")
+    if Groq is None:
+        raise RuntimeError("Package 'groq' not installed. Add 'groq' to requirements.txt.")
+    return Groq(api_key=api_key)
+
+def chat_once(messages: List[Dict[str, str]],
+              model: str = DEFAULT_MODEL,
+              temperature: float = 0.6,
+              top_p: float = 0.9,
+              max_tokens: int = 600) -> str:
+    client = get_client()
+    resp = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        temperature=temperature,
+        top_p=top_p,
+        max_tokens=max_tokens,
+    )
+    return resp.choices[0].message.content.strip()
+
+def generate_post(prompt: str,
+                  model: str,
+                  temperature: float,
+                  top_p: float,
+                  max_tokens: int) -> str:
+    messages = [
+        {"role": "system", "content": "You craft concise, original, high-signal LinkedIn posts. Respond with plain text only."},
+        {"role": "user", "content": prompt},
+    ]
+    return chat_once(messages, model, temperature, top_p, max_tokens)
+
+def transform_post(instruction: str,
+                   post_text: str,
+                   model: str,
+                   temperature: float,
+                   top_p: float,
+                   max_tokens: int) -> str:
+    messages = [
+        {"role": "system", "content": "You are a precise LinkedIn editor. Respond with plain text only."},
+        {"role": "user", "content": f"Instruction:\n{instruction}\n\nPost:\n{post_text}"}
+    ]
+    return chat_once(messages, model, temperature, top_p, max_tokens)
+
+def generate_hooks(topic: str,
+                   audience: str,
+                   tone: str,
+                   count: int,
+                   model: str,
+                   temperature: float,
+                   top_p: float,
+                   max_tokens: int) -> str:
+    messages = [
+        {"role": "system", "content": "You generate punchy first lines for viral LinkedIn posts."},
+        {"role": "user", "content": f"Create {count} distinct, curiosity-driving first lines for a post.\nTopic: {topic}\nAudience: {audience}\nTone: {tone}\nRules: 1 line each, no labels, no emojis."}
+    ]
+    return chat_once(messages, model, temperature, top_p, max_tokens)