Update app.py

app.py

@@ -1,14 +1,13 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import pipeline
 
-# Load the model and tokenizer from Hugging Face
+# Load the pipeline from Hugging Face
 @st.cache_resource
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained("kshitij230/Eunoia")
-    model = AutoModelForCausalLM.from_pretrained("kshitij230/Eunoia")
-    return tokenizer, model
+def load_pipeline():
+    pipe = pipeline("text-generation", model="kshitij230/Eunoia")
+    return pipe
 
-tokenizer, model = load_model()
+pipe = load_pipeline()
 
 # Streamlit page config
 st.set_page_config(page_title="Eunoia 💜", layout="centered")
@@ -32,32 +31,28 @@ if user_input:
     st.chat_message("user").markdown(user_input)
     st.session_state.messages.append({"role": "user", "content": user_input})
 
-    # Build conversation prompt (simple format)
+    # Build conversation prompt
     prompt = ""
     for msg in st.session_state.messages:
         role = "User" if msg["role"] == "user" else "Bot"
         prompt += f"{role}: {msg['content']}\n"
     prompt += "Bot:"
 
-    # Tokenize the input prompt
-    inputs = tokenizer(prompt, return_tensors="pt")
-
     # Generate model response
     with st.spinner("Eunoia is listening..."):
-        outputs = model.generate(
-            inputs['input_ids'],
+        response = pipe(
+            prompt,
             max_length=200,
-            pad_token_id=tokenizer.eos_token_id,
             do_sample=True,
             temperature=0.7,
             top_k=50,
             top_p=0.95,
             repetition_penalty=1.2,
-            stop_token=tokenizer.eos_token
-        )
-        
-        reply = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        reply = reply[len(prompt):].strip()  # Remove the prompt portion from the reply
+            pad_token_id=pipe.tokenizer.eos_token_id
+        )[0]["generated_text"]
+
+        # Remove the prompt from the response
+        reply = response[len(prompt):].strip()
 
     # Display bot reply
     st.chat_message("assistant").markdown(reply)