added gemma

app.py

@@ -7,9 +7,21 @@ from langchain.tools import Tool
 from langchain_huggingface import HuggingFacePipeline
 import os
 from dotenv import load_dotenv
+from transformers import AutoModelForCausalLM, AutoTokenizer,pipeline
+import torch
 
 load_dotenv()
 NEWSAPI_KEY = os.getenv("NEWSAPI_KEY")
+access_token = os.getenv("API_KEY")
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", token = access_token)
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b-it",
+    torch_dtype=torch.bfloat16,
+    token = access_token
+)
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=100, top_k=50, temperature=0.1)
+
 
 def fetch_stock_data(ticker):
     print("fetching stock data")
@@ -34,15 +46,7 @@ def calculate_moving_average(ticker, window=5):
     hist[f"{window}-day MA"] = hist["Close"].rolling(window=window).mean()
     return hist[["Close", f"{window}-day MA"]].tail(5)
 
-llm = HuggingFacePipeline.from_model_id(
-    model_id="microsoft/Phi-3-mini-4k-instruct",
-    task="text-generation",
-    pipeline_kwargs={
-        "max_new_tokens": 100,
-        "top_k": 50,
-        "temperature": 0.1,
-    },
-)
+llm = HuggingFacePipeline(pipeline=pipe)
 
 stock_data_tool = Tool(
     name="Stock Data Fetcher",
@@ -80,6 +84,7 @@ if st.button("Submit"):
         with st.spinner("Processing..."):
             try:
                 response = agent.run(query)
+                print(f"Response: {response}")
                 st.success("Response:")
                 st.write(response)
             except Exception as e: