Update app.py

app.py

@@ -19,13 +19,12 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig,
 ###############
 os.system("pip install -r requirements.txt")
 
-#some model
-#tokenizer = AutoTokenizer.from_pretrained("bofenghuang/vigogne-2-7b-chat")
-model_name_or_path = "bofenghuang/vigogne-2-7b-chat"
-revision = "v2.0"
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, revision=revision, padding_side="right", use_fast=False)
-model = AutoModelForCausalLM.from_pretrained(model_name_or_path, revision=revision)
+model = AutoModelForCausalLM.from_pretrained("Writer/palmyra-small")
+
+tokenizer = AutoTokenizer.from_pretrained("Writer/palmyra-small")
 
 
 st.set_page_config(page_title="Gemini RAG", layout="wide")
@@ -142,8 +141,8 @@ def user_input(user_question, api_key):
     chain = get_conversational_chain()
     response_gemini = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
     
-    # Initialize the Hugging Face conversational pipeline with your custom model
-    pipeline = ConversationalPipeline(model_name_or_path="bofenghuang/vigogne-2-7b-chat")
+    # Initialize the Hugging Face text generation pipeline with your custom model
+    pipeline = TextGeneratorPipeline(model="Writer/palmyra-small")
 
     # Prompt template for making the response more conversational
     prompt_template = f"""
@@ -166,6 +165,7 @@ def user_input(user_question, api_key):
 
 
 
+
 def main():
     st.header("RAG based LLM Application")