Update app.py

app.py

@@ -1,14 +1,19 @@
 import streamlit as st 
-from langchain.vectorstores import faiss
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
-from langchain.vectorstores import FAISS
 from langchain.document_loaders import TextLoader
 from langchain.embeddings import SentenceTransformerEmbeddings
 from tempfile import NamedTemporaryFile
 import os
 import shutil
-
+from typing import Any, List, Mapping, Optional
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from gradio_client import Client
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.vectorstores import FAISS
+import time
 try:
     shutil.rmtree("tempDir")
 except :
@@ -17,28 +22,130 @@ try:
     os.mkdir("tempDir")
 except:
     pass
+css = '''
+<style>
+.chat-message {
+    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
+}
+.chat-message.user {
+    background-color: #2b313e
+}
+.chat-message.bot {
+    background-color: #475063
+}
+.chat-message .avatar {
+  width: 20%;
+}
+.chat-message .avatar img {
+  max-width: 78px;
+  max-height: 78px;
+  border-radius: 50%;
+  object-fit: cover;
+}
+.chat-message .message {
+  width: 80%;
+  padding: 0 1.5rem;
+  color: #fff;
+}
+'''
+
+bot_template = '''
+<div class="chat-message bot">
+    <div class="avatar">
+        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
+    </div>
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+
+user_template = '''
+<div class="chat-message user">
+    <div class="avatar">
+        <img src="https://cdn-icons-png.flaticon.com/512/149/149071.png">
+    </div>    
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+
+
 def save_uploadedfile(uploadedfile):
 
+
     with open(os.path.join("tempDir",uploadedfile.name),"wb") as f:
          f.write(uploadedfile.getbuffer())
     return st.success("Saved File:{} to tempDir".format(uploadedfile.name))
 
+def ricerca_llama(domanda):
+    client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/")
+    risultato = client.predict( str(domanda),"you are a university professor, use appropriate language to answer students' questions .",0.1,2000,0.1,1.1,api_name="/chat")
+    print(domanda)
+    risultato=str(risultato).split("<")[0]
+    return risultato
+
 
+
+class CustomLLM(LLM):
+    @property
+    def _llm_type(self) -> str:
+        return "custom"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        if stop is not None:
+            raise ValueError("stop kwargs are not permitted.")
+        
+        # Chiamata alla tua funzione API
+        risultato = ricerca_llama(prompt)  # Assumendo che `prompt` sia la descrizione wiki
+        
+        return risultato
+
+def hande_user_input(user_question):
+    response=st.session_state.conversation({"question":user_question})
+    st.session_state.chat_history= response["chat_history"]
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2== 0:
+            st.write(user_template.replace("{{MSG}}",message.content),unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace("{{MSG}}",message.content),unsafe_allow_html=True)
+
+
+def get_conversation_chain(vectorstore):
+    llm=CustomLLM()
+    memory=ConversationBufferMemory(memory_key="chat_history",return_messages=True)
+    conversation_chain= ConversationalRetrievalChain.from_llm(llm=llm,
+        retriever=vectorstore.as_retriever(),
+        memory=memory)
+    return conversation_chain
 def main():
-    st.set_page_config(page_title="chet with unipv")
-    st.text_input("fai una domanda al tuo professore ")
+
+    st.set_page_config(page_title="chat with unipv")
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation= None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history= None
+        
+    st.write(css,unsafe_allow_html=True)
+    user_input=st.text_input("fai una domanda al tuo professore ")
+    if user_input:
+        hande_user_input(user_input)
+
     with st.sidebar:
-        st.subheader("Your_faiss_index")
+        st.subheader("Your faiss index")
         documents=st.file_uploader("upload your faiss index here ",accept_multiple_files=True)
         if st.button("Procedi"):
-            for document in documents:
-                save_uploadedfile(document)
-            #with st.spinner("sto processando i tuoi dati"):
-            print(documents)
-            query="chi è matteo salvini?"
-            embeddings= HuggingFaceInstructEmbeddings(model_name="thenlper/gte-base")
-            new_db = FAISS.load_local("tempDir", embeddings)
-            docs = new_db.similarity_search(query)
-            print(docs)
+            with st.spinner("sto processando i tuoi dati"):
+                for document in documents:
+                    save_uploadedfile(document)
+                    time.sleep(1)
+                embeddings= HuggingFaceInstructEmbeddings(model_name="thenlper/gte-base")
+                new_db = FAISS.load_local("tempDir", embeddings)
+            st.session_state.conversation=get_conversation_chain(new_db)
+            #conversation=get_conversation_chain(new_db)
 if __name__=="__main__":
     main()