Update app.py

app.py

@@ -1,39 +1,51 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+# from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 import transformers
 import torch
 from huggingface_hub import login
 from langchain.llms import HuggingFacePipeline
-
+from transformers import AutoTokenizer, AutoModelForCausalLM
 # login(token=token)
 
 def greet(name):
     return str(int(name)+10)
 
 
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+
+
+    
 # Load model directly
 # Load model directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
+# from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
+# tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+# model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
 
 
 # model = "meta-llama/Llama-2-13b-chat-hf" # meta-llama/Llama-2-7b-hf
 # 
 # tokenizer = AutoTokenizer.from_pretrained(model, use_auth_token=True)
 
-pipe = pipeline(
-    "text2text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_length=512,
-    temperature=0.5,
-    top_p=0.95,
-    repetition_penalty=1.15
-)
-
-local_llm = HuggingFacePipeline(pipeline=pipe)
+# pipe = pipeline(
+#     "text2text-generation",
+#     model=model,
+#     tokenizer=tokenizer,
+#     max_length=512,
+#     temperature=0.5,
+#     top_p=0.95,
+#     repetition_penalty=1.15
+# )
+
+# local_llm = HuggingFacePipeline(pipeline=pipe)
 
 # def get_llama_response(prompt: str) -> None:
 #     """