Update app.py

app.py

@@ -1,99 +1,93 @@
-#from langchain import PromptTemplate
-from langchain_core.prompts import PromptTemplate
-import os 
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain_community.llms.ctransformers import CTransformers
-#from langchain.chains import RetrievalQA
-from langchain.chains.retrieval_qa.base import RetrievalQA
-import chainlit as cl
-
-DB_FAISS_PATH = 'vectorstores/'
-
-custom_prompt_template = '''
-use the following pieces of information to answer the user's questions/
-If you don't know the answer, please just say that don't know the answer, don't try to make uo an answer.
-
-Content : {}
-Question : {question}
-
-only return the helpful answer below and nothing else.
-'''
-
-def set_custom_prompt():
-    """
-    Prompt template for QA retrieval for vector stores
-    """
-    prompt = PromptTemplate(template = custom_prompt_template,
-                            input_variables = ['context','question'])
-    
-    return prompt
-
-def load_llm():
-    llm = CTransformers(
-        model = 'TheBloke/Llama-2-7B-Chat-GGML',
-        #model = AutoModel.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML"),
-        model_type = 'llama',
-        max_new_token = 512,
-        temperature = 0.5
-    )
-    return llm
-
-def retrieval_qa_chain(llm,prompt,db):
-    qa_chain = RetrievalQA.from_chain_type(
-        llm = llm,
-        chain_type = 'stuff',
-        retriever = db.as_retriever(search_kwargs= {'k': 2}),
-        return_source_documents = True,
-        chain_type_kwargs = {'prompt': prompt}
-    )
-
-    return qa_chain
-
-def qa_bot():
-    embeddings = HuggingFaceBgeEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2',
-                                          model_kwargs = {'device':'cpu'})
-    
-    # index_path = os.path.join('vectorstores')
-    # if not os.path.exists(index_path):
-    #     raise FileNotFoundError(f"FAISS index file not found at {index_path}")
-
-    # Load the index if it exists
-    #db = FAISS.load_local('vectorstores', embeddings, allow_dangerous_deserialization=True)
-    
-    db = FAISS.load_local(DB_FAISS_PATH, embeddings,allow_dangerous_deserialization=True)
-    llm = load_llm()
-    qa_prompt = set_custom_prompt()
-    qa = retrieval_qa_chain(llm,qa_prompt, db)
-
-    return qa
-
-def final_result(query):
-    qa_result = qa_bot()
-    response = qa_result({'quert' : query})
-
-    return response
-
-
-import streamlit as st
-
-# Initialize the bot
-bot = qa_bot()
-
-def process_query(query):
-    # Here you would include the logic to process the query and return a response
-    response, sources = bot.answer_query(query)  # Modify this according to your bot implementation
-    if sources:
-        response += f"\nSources: {', '.join(sources)}"
-    else:
-        response += "\nNo Sources Found"
-    return response
-
-# Setting up the Streamlit app
-st.title('Medical Chatbot')
-
-user_input = st.text_input("Hi, welcome to the medical Bot. What is your query?")
-
-if user_input:
-    output = process_query(user_input)
+#from langchain import PromptTemplate
+from langchain_core.prompts import PromptTemplate
+import os 
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms.ctransformers import CTransformers
+#from langchain.chains import RetrievalQA
+from langchain.chains.retrieval_qa.base import RetrievalQA
+import chainlit as cl
+
+DB_FAISS_PATH = 'vectorstores/'
+
+custom_prompt_template = '''
+use the following pieces of information to answer the user's questions/
+If you don't know the answer, please just say that don't know the answer, don't try to make uo an answer.
+
+Context : {}
+Question : {question}
+
+only return the helpful answer below and nothing else.
+'''
+
+def set_custom_prompt():
+    """
+    Prompt template for QA retrieval for vector stores
+    """
+    prompt = PromptTemplate(template = custom_prompt_template,
+                            input_variables = ['context','question'])
+    
+    return prompt
+
+def load_llm():
+    llm = CTransformers(
+        model = 'TheBloke/Llama-2-7B-Chat-GGML',
+        #model = AutoModel.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML"),
+        model_type = 'llama',
+        max_new_token = 512,
+        temperature = 0.5
+    )
+    return llm
+
+def retrieval_qa_chain(llm,prompt,db):
+    qa_chain = RetrievalQA.from_chain_type(
+        llm = llm,
+        chain_type = 'stuff',
+        retriever = db.as_retriever(search_kwargs= {'k': 2}),
+        return_source_documents = True,
+        chain_type_kwargs = {'prompt': prompt}
+    )
+
+    return qa_chain
+
+def qa_bot():
+    embeddings = HuggingFaceBgeEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2',
+                                          model_kwargs = {'device':'cpu'})
+    
+    
+    db = FAISS.load_local(DB_FAISS_PATH, embeddings,allow_dangerous_deserialization=True)
+    llm = load_llm()
+    qa_prompt = set_custom_prompt()
+    qa = retrieval_qa_chain(llm,qa_prompt, db)
+
+    return qa
+
+def final_result(query):
+    qa_result = qa_bot()
+    response = qa_result({'quert' : query})
+
+    return response
+
+
+import streamlit as st
+
+# Initialize the bot
+bot = qa_bot()
+
+def process_query(query):
+    # Here you would include the logic to process the query and return a response
+    response, sources = bot.answer_query(query)  # Modify this according to your bot implementation
+    if sources:
+        response += f"\nSources: {', '.join(sources)}"
+    else:
+        response += "\nNo Sources Found"
+    return response
+
+# Setting up the Streamlit app
+st.title('Medical Chatbot')
+
+user_input = st.text_input("Hi, welcome to the medical Bot. What is your query?")
+
+if user_input:
+    output = process_query(user_input)
     st.text_area("Response", output, height=300)