Create app.py

app.py

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+import os
+#from dotenv import load_dotenv
+import streamlit as st
+
+# from genai.credentials import Credentials
+# from genai.schemas import GenerateParams
+# from genai.model import Model
+
+from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes
+from ibm_watson_machine_learning.foundation_models import Model
+from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
+from langchain_ibm import WatsonxLLM
+
+
+#load_dotenv()
+# api_key = os.getenv("GENAI_KEY", None)
+# api_endpoint = os.getenv("GENAI_API", None)
+
+# creds = Credentials(api_key,api_endpoint)
+
+# params = GenerateParams(
+#     decoding_method="sample",
+#     max_new_tokens=200,
+#     min_new_tokens=1,
+#     stream=False,
+#     temperature=0.7,
+#     top_k=50,
+#     top_p=1,
+#     stop_sequences= ["Human:","AI:"],
+# )
+
+"""api_key =   os.getenv("API_KEY", None)
+project_id = os.getenv("PROJECT_ID", None)"""
+
+api_key="be2PIyqJbKm9LwNDRvWeA8Bp2Dc2TQt30rw2OAd-PPKf"
+project_id="11b29d9b-5940-41da-a2c7-86a17f270bf7"
+creds = {
+    "url"    : "https://us-south.ml.cloud.ibm.com",
+    "apikey" : api_key
+}
+
+params = {
+    GenParams.DECODING_METHOD:"sample",
+    GenParams.MAX_NEW_TOKENS:200,
+    GenParams.MIN_NEW_TOKENS:1,
+    GenParams.TEMPERATURE:0.7,
+    GenParams.TOP_K:50,
+    GenParams.TOP_P:1,
+    GenParams.STOP_SEQUENCES: ["Human:","AI:"]
+}
+
+with st.sidebar:
+    st.title("WATSONX CHAT")
+    st.write("WATSONX.AI")
+    st.write("RAHMAN")
+
+st.title("CHAT WITH WATSONX")
+
+with st.chat_message("system"):
+    st.write("Hello 👋, lets chat with watsonx")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+llm = Model(ModelTypes.LLAMA_2_70B_CHAT,creds,params,project_id)
+
+# llm = Model(model="meta-llama/llama-2-7b-chat",credentials=creds, params=params)
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Say something"):
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    prompttemplate = f"""
+    [INST]<<SYS>>Respond in English<<SYS>>
+    {prompt}
+    [/INST]
+    """
+    response_text = llm.generate_text(prompttemplate)
+    answer = response_text
+    # for response in response_text[0].generated_text
+    #     answer += response[0].generated_text
+
+    st.session_state.messages.append({"role": "agent", "content": answer}) 
+
+    with st.chat_message("agent"):
+        st.markdown(answer)