basic working

.env

@@ -1 +1,2 @@
-OPENAI_API_KEY=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
+OPENAI_API_KEY=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
+REPLICATE_API_TOKEN=r8_4fktoXrDGkgHY8uw1XlVtQJKQlAILKv0iBmPI

app.py

@@ -1,4 +1,5 @@
 import streamlit as st
+import os
 from PyPDF2 import PdfReader
 from streamlit_extras.add_vertical_space import add_vertical_space
 from langchain.text_splitter import RecursiveCharacterTextSplitter
@@ -21,16 +22,51 @@ with st.sidebar:
             length_function=len
         )
         chunks=text_splitter.split_text(text)
-        #embeddings=OpenAIEmbeddings()
-        #vectorstore=faiss.FAISS.from_texts(chunks, embedding=embeddings)
+           
         
-        
-        
-        st.write(chunks)
     st.write('Made by ALOK')
+    
 
 def main():
+    import replicate
     st.header('Talk to your file')
+    #embeddings=OpenAIEmbeddings()
+    #vectorstore=faiss.FAISS.from_texts(chunks, embedding=embeddings)
+    
+    # The meta/llama-2-70b-chat model can stream output as it's running.
+      
+    
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # 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"])
+
+    # React to user input
+    if prompt := st.chat_input("Type Here"):
+        # Display user message in chat message container
+        st.chat_message("user").markdown(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        replicate = replicate.Client(api_token='r8_4fktoXrDGkgHY8uw1XlVtQJKQlAILKv0iBmPI')
+        output = replicate.run(
+        "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
+        input={"prompt": prompt}
+        )
+        # The predict method returns an iterator, and you can iterate over that output.
+        res=''
+        for item in output:
+            res+=item
+        response = f"AI: {res}"
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            st.markdown(response)
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})
+            # https://replicate.com/meta/llama-2-70b-chat/versions/02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3/api#output-schema
+            #print(item, end="")
     
 if __name__=='__main__':
     main()