Initial Space setup

app.py

@@ -2,71 +2,37 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 from peft import PeftModel
 
-# 1) Base model & tokenizer
+# 1) Load the original base model & tokenizer
 BASE_MODEL = "facebook/blenderbot-400M-distill"
 tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
 base_model = AutoModelForSeq2SeqLM.from_pretrained(BASE_MODEL)
 
-# 2) Attach your LoRA adapter
+# 2) Load your fine-tuned LoRA adapter on top
 ADAPTER_REPO = "abinashnp/bayedger-chatbot"
 model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
 
-# 3) Build the text2text pipeline (no explicit device arg)
+# 3) Wrap that in a text2text pipeline
 chatbot = pipeline(
     "text2text-generation",
     model=model,
     tokenizer=tokenizer,
-    # device_map="auto"  # only if you use Accelerate; otherwise remove
-)
-
-# 4) System prompt (context) that always precedes user questions
-SYSTEM_PROMPT = (
-    "You are BayEdger’s AI assistant. You only answer FAQs about BayEdger’s "
-    "services, pricing, and contact info. If you don’t know the answer, "
-    "you must say exactly:\n"
-    '"Sorry, I don’t have that info—please contact [email protected]."\n\n'
-    "Here is what you should know about BayEdger:\n"
-    "- AI‐powered websites and automation\n"
-    "- Chatbots, email agents, process automation, analytics, content gen\n"
-    "- Clear pricing tiers: Basic site ($400), Chatbot ($750+50/mo), Email ($1k+100/mo), etc.\n"
-    "- Starter/Growth/Premium bundles\n"
-    "- Contact: [email protected], +1‐234‐559‐87994, 13 Madison St, NY\n\n"
+    device_map="auto",       # leave out device arg when using accelerate device_map
 )
 
 def respond(query):
-    # Build the prompt correctly by concatenating
-    prompt = (
-        SYSTEM_PROMPT
-        + f"question: {query}\n"
-        + "answer:"
-    )
-
     out = chatbot(
-        prompt,
-        max_new_tokens=128,
-        do_sample=False,
-        num_beams=2,
-        early_stopping=True,
-        pad_token_id=tokenizer.eos_token_id
+        f"question: {query} answer:",
+        max_new_tokens=150,
+        temperature=1.0,
+        top_p=0.9,
+        repetition_penalty=1.1,
+        num_beams=1
     )[0]["generated_text"]
+    return out
 
-    # Strip off the “answer:” prefix
-    if "answer:" in out:
-        reply = out.split("answer:", 1)[1].strip()
-    else:
-        reply = out.strip()
-
-    # Fallback for unknowns
-    if len(reply) < 15 or "don't know" in reply.lower() or "sorry" in reply.lower():
-        return "Sorry, I don’t have that info—please contact [email protected]."
-
-    return reply
-
-
-# 9) Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 BayEdger FAQ Chatbot")
-    txt = gr.Textbox(placeholder="Ask me about BayEdger…", label="Your question")
+    gr.Markdown("# 🤖 Bayedger FAQ Chatbot")
+    txt = gr.Textbox(label="Ask me anything")
     out = gr.Textbox(label="Answer")
     txt.submit(respond, txt, out)