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README.md

@@ -1,8 +1,8 @@
 ---
-title: LLMsIntro
-emoji: 🏃
-colorFrom: green
-colorTo: purple
+title: ChatModelApp
+emoji: ⚡
+colorFrom: gray
+colorTo: red
 sdk: streamlit
 sdk_version: 1.21.0
 app_file: app.py

app.py

@@ -1,47 +1,59 @@
-#Hello! It seems like you want to import the Streamlit library in Python. Streamlit is a powerful open-source framework used for building web applications with interactive data visualizations and machine learning models. To import Streamlit, you'll need to ensure that you have it installed in your Python environment.
-#Once you have Streamlit installed, you can import it into your Python script using the import statement,
 
 import streamlit as st
 
-from langchain_openai import OpenAI
-from langchain_huggingface import HuggingFaceEndpoint
 
-#When deployed on huggingface spaces, this values has to be passed using Variables & Secrets setting, as shown in the video :)
-#import os
-#os.environ["OPENAI_API_KEY"] = "sk-PLfFwPq6y24234234234FJ1Uc234234L8hVowXdt"
+from langchain_openai import ChatOpenAI
 
 #import os
-#os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
+#os.environ["OPENAI_API_KEY"] = "sk-PLfFw23dd932dfg34446dftyvvdfgdfgmvXr2dL8hVowXdt"
+
+
+from langchain.schema import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage
+)
+
+# From here down is all the StreamLit UI
+st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
+st.header("Hey, I'm your Chat GPT")
+
+
+
+if "sessionMessages" not in st.session_state:
+     st.session_state.sessionMessages = [
+        SystemMessage(content="You are a helpful assistant.")
+    ]
+
+
 
-#Function to return the response
 def load_answer(question):
-    #llm = OpenAI(model_name="gpt-3.5-turbo-instruct",temperature=0)
-    llm = HuggingFaceEndpoint(
-    repo_id="mistralai/Mistral-7B-Instruct-v0.3") # Model link : https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
 
-    answer=llm.invoke(question)
-    return answer
+    st.session_state.sessionMessages.append(HumanMessage(content=question))
 
+    assistant_answer  = chat.invoke(st.session_state.sessionMessages )
+
+    st.session_state.sessionMessages.append(AIMessage(content=assistant_answer.content))
+
+    return assistant_answer.content
 
-#App UI starts here
-st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
-st.header("LangChain Demo")
 
-#Gets the user input
 def get_text():
-    input_text = st.text_input("You: ", key="input")
+    input_text = st.text_input("You: ")
     return input_text
 
 
-user_input=get_text()
-if user_input!= "":
-    response = load_answer(user_input)
+chat = ChatOpenAI(temperature=0)
+
 
+
+
+user_input=get_text()
 submit = st.button('Generate')  
 
-#If generate button is clicked
 if submit:
-
+    
+    response = load_answer(user_input)
     st.subheader("Answer:")
 
     st.write(response)