Initial Space setup

app.py

@@ -1,27 +1,38 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from peft import PeftModel
 
-# Load your fine-tuned model from the Hub
+# 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) Load your fine-tuned LoRA adapter on top
+ADAPTER_REPO = "abinashnp/bayedger-chatbot"
+model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
+
+# 3) Wrap that in a text2text pipeline
 chatbot = pipeline(
     "text2text-generation",
-    model="abinashnp/bayedger-chatbot",  # or your fine-tuned model ID
-    tokenizer="abinashnp/bayedger-chatbot",
+    model=model,
+    tokenizer=tokenizer,
+    device_map="auto",       # leave out device arg when using accelerate device_map
 )
 
 def respond(query):
-    # generate response
-    out = chatbot(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"]
+    out = chatbot(
+        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
 
-# Build Gradio interface sss
 with gr.Blocks() as demo:
     gr.Markdown("# 🤖 Bayedger FAQ Chatbot")
-    txt = gr.Textbox(label="Ask me anything", placeholder="Type your question here…")
+    txt = gr.Textbox(label="Ask me anything")
     out = gr.Textbox(label="Answer")
     txt.submit(respond, txt, out)