Rename app.py to app1.py

app.py → app1.py

@@ -1,82 +1,110 @@
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.callbacks.manager import CallbackManager
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from langchain_community.llms import LlamaCpp
-from langchain.memory import ConversationBufferMemory
+from langchain.memory import ConversationBufferWindowMemory
 from langchain.chains import ConversationalRetrievalChain
 import streamlit as st
-from langchain.callbacks import StreamlitCallbackHandler
 import time
-st.set_page_config(layout="wide")
+
+col1, col2, col3 = st.columns([1,4,1])
+with col2:
+    st.image("https://github.com/harshitv804/MedChat/assets/100853494/0aa18d7e-5305-4d8e-89d8-09fffce1589e")
+
+st.markdown(
+    """
+    <style>
+div.stButton > button:first-child {
+    background-color: #ffd0d0;
+}
+div.stButton > button:active {
+    background-color: #ff6262;
+}
+
+   div[data-testid="stStatusWidget"] div button {
+        display: none;
+        }
+    
+    .reportview-container {
+            margin-top: -2em;
+        }
+        #MainMenu {visibility: hidden;}
+        .stDeployButton {display:none;}
+        footer {visibility: hidden;}
+        #stDecoration {display:none;}
+    button[title="View fullscreen"]{
+    visibility: hidden;}
+        </style>
+""",
+    unsafe_allow_html=True,
+)
+
+def reset_conversation():
+  st.session_state.messages = []
+  st.session_state.memory.clear()
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "memory" not in st.session_state:
+    st.session_state.memory = ConversationBufferWindowMemory(k=3, memory_key="chat_history",return_messages=True) 
 
 embeddings = HuggingFaceEmbeddings(model_name="BAAI/llm-embedder")
 db = FAISS.load_local("faiss_index", embeddings)
 db_retriever = db.as_retriever(search_type="similarity",search_kwargs={"k": 3})
-callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
 
 llm = LlamaCpp(
-model_path="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
+model_path=r"C:\Users\TESS\Downloads\stablelm-zephyr-3b.Q4_K_M.gguf",
 temperature=0.75,
 max_tokens=2000,
 n_ctx = 4000,
-top_p=1,
-n_gpu_layers=10)
+top_p=1)
 
-custom_prompt_template = """You are a medical practitioner who provides right medical information. Use the given following pieces of information to answer the user's question correctly. Add additional information from your knowledge base if necessary. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+custom_prompt_template = """You are a medical practitioner who provides right medical information. Use the given following pieces of information to answer the user's question correctly. If you don't know the answer, just say that you don't know, don't try to make up an answer. Utilize the provided knowledge base and search for relevant information. Follow and answer the question format closely. Give only the important information. The information should be abstract, high quality content and comprehensive.
 
 Context: {context}
 
-Current conversation: {chat_history}
+History: {chat_history}
 
-Human: {question}
-Only return the helpful answer below and nothing else.
+Question: {question}
 
-AI Assistant:
+Answer:
 """
 prompt = PromptTemplate(template=custom_prompt_template,
-                        input_variables=['context', 'question','chat_history'])
+                        input_variables=['context', 'question', 'chat_history'])
 
 qa = ConversationalRetrievalChain.from_llm(
     llm=llm,
-    memory=ConversationBufferMemory(
-        memory_key="chat_history",
-        ai_prefix="AI Assistant",
-        return_messages=True
-    ),
+    memory=st.session_state.memory,
     retriever=db_retriever,
     combine_docs_chain_kwargs={'prompt': prompt}
 )
 
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# st.write(st.session_state.messages)
-
 for message in st.session_state.messages:
     with st.chat_message(message.get("role")):
         st.write(message.get("content"))
 
-prompt = st.chat_input("Say something")
+input_prompt = st.chat_input("Say something")
 
-if prompt:
+if input_prompt:
     with st.chat_message("user"):
-        st.write(prompt)
+        st.write(input_prompt)
 
-    st.session_state.messages.append({"role":"user","content":prompt})
+    st.session_state.messages.append({"role":"user","content":input_prompt})
 
     with st.chat_message("assistant"):
-        with st.status("Thinking...",expanded=True):
-            st_callback = StreamlitCallbackHandler(st.container())
-            result = qa.invoke(input=prompt,callbacks=[st_callback])
-            st.session_state.messages.append({"role":"assistant","content":result["answer"]})
+        with st.status("Thinking 💡...",expanded=True):
+            result = qa.invoke(input=input_prompt)
+
             message_placeholder = st.empty()
-            full_response = ""
 
-        for chunk in result["answer"].split():
-            full_response+=chunk+" "
-            time.sleep(0.10)
+            full_response = "⚠️ **_Note: Information provided may be inaccurate. Consult a qualified doctor for accurate advice._** \n\n\n"
+        for chunk in result["answer"]:
+            full_response+=chunk
+            time.sleep(0.02)
+            
+            message_placeholder.markdown(full_response+" ▌")
+        st.button('Reset All Chat 🗑️', on_click=reset_conversation)
 
-            message_placeholder.markdown(full_response)
+    st.session_state.messages.append({"role":"assistant","content":result["answer"]})