Update

app.py

@@ -1,6 +1,26 @@
 import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-st.title("Echo Bot")
+st.set_page_config(page_title="Hugging Face Chatbot", layout="centered")
+st.title("Hugging Face Chatbot")
+
+@st.cache_resource
+def load_model():
+    # Load tokenizer and model from Hugging Face
+    tokenizer = AutoTokenizer.from_pretrained("Grandediw/lora_model_finetuned", use_fast=True)
+    model = AutoModelForCausalLM.from_pretrained("Grandediw/lora_model_finetuned", device_map="auto", trust_remote_code=True)
+    chat_pipeline = pipeline(
+        "text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        max_length=512,
+        temperature=0.7,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    return chat_pipeline
+
+chat_pipeline = load_model()
 
 # Initialize chat history
 if "messages" not in st.session_state:
@@ -11,16 +31,24 @@ for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
-# React to user input
-if prompt := st.chat_input("What is up?"):
-    # Display user message in chat message container
+# User input
+if prompt := st.chat_input("Ask me anything:"):
+    # Display user message and store it
     st.chat_message("user").markdown(prompt)
-    # Add user message to chat history
     st.session_state.messages.append({"role": "user", "content": prompt})
 
-    response = f"Echo: {prompt}"
-    # Display assistant response in chat message container
+    # Generate response
+    with st.spinner("Thinking..."):
+        # Using the pipeline to generate a response
+        response = chat_pipeline(prompt)[0]["generated_text"]
+        
+        # The model may return the prompt + response concatenated, so you might need
+        # to extract only the response part. This depends on how the model is trained.
+        # Here we assume the model returns the full text and we just remove the original prompt from it:
+        if response.startswith(prompt):
+            response = response[len(prompt):].strip()
+
+    # Display and store assistant response
     with st.chat_message("assistant"):
         st.markdown(response)
-    # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": response})