Update app.py

app.py

@@ -5,6 +5,7 @@ import streamlit as st
 from langchain.agents import AgentType, initialize_agent, load_tools
 from langchain.llms import OpenAI as l_OpenAI
 from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 from helpers.foundation_models import *
 
@@ -13,6 +14,26 @@ SERPAPI_API_KEY = os.environ["SERPAPI_API_KEY"]
 openai_client = openai.OpenAI(api_key=OPENAI_API_KEY)
 
 
+tokenizer = AutoTokenizer.from_pretrained("eagle0504/llama-2-7b-miniguanaco")
+model = AutoModelForCausalLM.from_pretrained("eagle0504/llama-2-7b-miniguanaco")
+
+
+def generate_response_from_llama2(query):
+
+    # Tokenize the input text
+    input_ids = tokenizer.encode(query, return_tensors="pt")
+
+    # Generate a response
+    # Adjust the parameters like max_length according to your needs
+    output = model.generate(input_ids, max_length=50, num_return_sequences=1, temperature=0.7)
+
+    # Decode the output to human-readable text
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    # output
+    return generated_text
+
+
 # Initialize chat history
 if "messages" not in st.session_state:
     st.session_state.messages = []
@@ -38,7 +59,7 @@ with st.expander("Instructions"):
 
 option = st.sidebar.selectbox(
     "Which task do you want to do?",
-    ("Sentiment Analysis", "Medical Summarization", "ChatGPT", "ChatGPT (with Google)"),
+    ("Sentiment Analysis", "Medical Summarization", "Llama2", "ChatGPT", "ChatGPT (with Google)"),
 )
 
 
@@ -78,6 +99,10 @@ if prompt := st.chat_input("What is up?"):
             if prompt:
                 out = pipe_summarization(prompt)
                 doc = out[0]["summary_text"]
+        elif option == "Llama2"
+            if prompt:
+                out = generate_response_from_llama2(query=prompt)
+                doc = out
         elif option == "ChatGPT":
             if prompt:
                 out = call_chatgpt(query=prompt)