p

app.py

@@ -3,67 +3,61 @@ import torch
 import os
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
-# ←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←
-# CRITICAL: Use secret token (THIS IS THE ONLY WAY THAT WORKS IN SPACES)
-# ←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←
-from huggingface_hub import login
-login(token=os.getenv("HF_TOKEN"))   # ← Go to Space Settings → Secrets → HF_TOKEN
+# GET TOKEN FROM SPACE SECRET (THIS IS THE ONLY WAY THAT WORKS)
+HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    raise ValueError("HF_TOKEN secret not found! Add it in Space Settings → Secrets")
 
-# Your full merged model (private is fine now)
-MODEL_REPO = "Chvigo/pkil-1.5B-merged-v1"
+# YOUR PRIVATE FULL MERGED MODEL
+MODEL_REPO = "Chvigo/pkil-1.5B"
 
-print("Loading tokenizer and model from private repo...")
+print("Loading tokenizer from private repo...")
 tokenizer = AutoTokenizer.from_pretrained(
     MODEL_REPO,
+    token=HF_TOKEN,                    # ← CRITICAL
     trust_remote_code=True,
-    token=os.getenv("HF_TOKEN")   # ← Force token here too
+    padding_side="left"
 )
+tokenizer.pad_token = tokenizer.eos_token
 
+print("Loading full merged model (1.5B pure weights)...")
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_REPO,
+    token=HF_TOKEN,                    # ← CRITICAL
     torch_dtype=torch.bfloat16,
     device_map="auto",
     trust_remote_code=True,
-    token=os.getenv("HF_TOKEN"),          # ← Critical line
     low_cpu_mem_usage=True
 )
 
 model.eval()
-print("Model loaded successfully — running full merged weights!")
+print("PKIL GOD MODE LOADED — FULL OWNERSHIP ACTIVE")
 
 generator = pipeline(
     "text-generation",
     model=model,
     tokenizer=tokenizer,
-    max_new_tokens=320,
+    max_new_tokens=350,
     temperature=0.75,
     top_p=0.90,
-    repetition_penalty=1.25,
+    repetition_penalty=1.28,
     do_sample=True,
     pad_token_id=tokenizer.eos_token_id
 )
 
-def generate_linkedin_post(prompt):
+def generate(prompt):
     messages = [
-        {"role": "system", "content": "You are the best LinkedIn copywriter alive. Write in short, powerful lines like Steve Jobs. Use 0–3 hashtags max. End with a real question 80% of the time. Be emotional, authentic, confident."},
+        {"role": "system", "content": "Write in short powerful lines. 0–3 hashtags max. End with question. Be emotional. No spam."},
         {"role": "user", "content": prompt}
     ]
-    output = generator(messages, return_full_text=False)
-    return output[0]["generated_text"].strip()
+    out = generator(messages, return_full_text=False)
+    return out[0]["generated_text"]
 
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# PKIL LinkedIn Ghostwriter\nFull 1.5B weights • No LoRA • Private repo")
-
-    prompt = gr.Textbox(label="Prompt", placeholder="Just got promoted after 3 years...", lines=4)
+    gr.Markdown("# PKIL LinkedIn Ghostwriter\nFull 1.5B • No LoRA • Private • God Mode")
+    txt = gr.Textbox(label="Prompt", lines=4, placeholder="Just got promoted after 3 years of silence...")
     btn = gr.Button("Generate →", variant="primary")
-    output = gr.Textbox(label="Your Post", lines=15)
-
-    btn.click(generate_linkedin_post, prompt, output)
-
-    gr.Examples([
-        ["Just got promoted after being rejected twice"],
-        ["Announce hitting $100k MRR bootstrapped"],
-        ["Finally said no to a toxic client"],
-    ], prompt)
+    out = gr.Textbox(label="Your Post", lines=16)
+    btn.click(generate, txt, out)
 
 demo.launch()