Update app.py

app.py

@@ -1,109 +1,109 @@
-import os
-
-import streamlit as st
-from langchain.chains import RetrievalQA
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.vectorstores.neo4j_vector import Neo4jVector
-from streamlit.logger import get_logger
-from chains import (
-    load_embedding_model,
-    load_llm,
-)
-
-url = os.getenv("NEO4J_URI")
-username = os.getenv("NEO4J_USERNAME")
-password = os.getenv("NEO4J_PASSWORD")
-ollama_base_url = os.getenv("OLLAMA_BASE_URL")
-embedding_model_name = os.getenv("EMBEDDING_MODEL", "SentenceTransformer" )
-llm_name = os.getenv("LLM", "llama2")
-url = os.getenv("NEO4J_URI")
-
-# Check if the required environment variables are set
-if not all([url, username, password,
-          ollama_base_url]):
-    st.write("The application requires some information before running.")
-    with st.form("connection_form"):
-        url = st.text_input("Enter NEO4J_URI",)
-        username = st.text_input("Enter NEO4J_USERNAME")
-        password = st.text_input("Enter NEO4J_PASSWORD", type="password")
-        ollama_base_url = st.text_input("Enter OLLAMA_BASE_URL")
-        st.markdown("Only enter the OPENAI_APIKEY to use OpenAI instead of Ollama. Leave blank to use Ollama.")
-        openai_apikey = st.text_input("Enter OPENAI_API_KEY", type="password")
-        submit_button = st.form_submit_button("Submit")
-    if submit_button:
-        if not all([url, username, password, ]):
-            st.write("Enter the Neo4j information.")
-        if not (ollama_base_url or openai_apikey):
-            st.write("Enter the Ollama URL or OpenAI API Key.")
-        if openai_apikey:
-            llm_name = "gpt-3.5"
-            os.environ['OPENAI_API_KEY'] = openai_apikey
-
-os.environ["NEO4J_URL"] = url
-
-logger = get_logger(__name__)
-
-embeddings, dimension = load_embedding_model(
-    embedding_model_name, config={"ollama_base_url": ollama_base_url}, logger=logger
-)
-
-
-class StreamHandler(BaseCallbackHandler):
-    def __init__(self, container, initial_text=""):
-        self.container = container
-        self.text = initial_text
-
-    def on_llm_new_token(self, token: str, **kwargs) -> None:
-        self.text += token
-        self.container.markdown(self.text)
-
-llm = load_llm(llm_name, logger=logger, config={"ollama_base_url": ollama_base_url})
-
-
-def main():
-        st.header("📄Chat with your pdf file")
-
-        # upload a your pdf file
-        pdf = st.file_uploader("Upload your PDF", type="pdf")
-
-        if pdf is not None:
-            pdf_reader = PdfReader(pdf)
-
-            text = ""
-            for page in pdf_reader.pages:
-                text += page.extract_text()
-
-            # langchain_textspliter
-            text_splitter = RecursiveCharacterTextSplitter(
-                chunk_size=1000, chunk_overlap=200, length_function=len
-            )
-
-            chunks = text_splitter.split_text(text=text)
-
-            # Store the chunks part in db (vector)
-            vectorstore = Neo4jVector.from_texts(
-                chunks,
-                url=url,
-                username=username,
-                password=password,
-                embedding=embeddings,
-                index_name="pdf_bot",
-                node_label="PdfBotChunk",
-                pre_delete_collection=True,  # Delete existing PDF data
-            )
-            qa = RetrievalQA.from_chain_type(
-                llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever()
-            )
-
-            # Accept user questions/query
-            query = st.text_input("Ask questions about your PDF file")
-
-            if query:
-                stream_handler = StreamHandler(st.empty())
-                qa.run(query, callbacks=[stream_handler])
-
-
-if __name__ == "__main__":
-     main()
+import os
+!pip install sentence-transformers
+import streamlit as st
+from langchain.chains import RetrievalQA
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.vectorstores.neo4j_vector import Neo4jVector
+from streamlit.logger import get_logger
+from chains import (
+    load_embedding_model,
+    load_llm,
+)
+
+url = os.getenv("NEO4J_URI")
+username = os.getenv("NEO4J_USERNAME")
+password = os.getenv("NEO4J_PASSWORD")
+ollama_base_url = os.getenv("OLLAMA_BASE_URL")
+embedding_model_name = os.getenv("EMBEDDING_MODEL", "SentenceTransformer" )
+llm_name = os.getenv("LLM", "llama2")
+url = os.getenv("NEO4J_URI")
+
+# Check if the required environment variables are set
+if not all([url, username, password,
+          ollama_base_url]):
+    st.write("The application requires some information before running.")
+    with st.form("connection_form"):
+        url = st.text_input("Enter NEO4J_URI",)
+        username = st.text_input("Enter NEO4J_USERNAME")
+        password = st.text_input("Enter NEO4J_PASSWORD", type="password")
+        ollama_base_url = st.text_input("Enter OLLAMA_BASE_URL")
+        st.markdown("Only enter the OPENAI_APIKEY to use OpenAI instead of Ollama. Leave blank to use Ollama.")
+        openai_apikey = st.text_input("Enter OPENAI_API_KEY", type="password")
+        submit_button = st.form_submit_button("Submit")
+    if submit_button:
+        if not all([url, username, password, ]):
+            st.write("Enter the Neo4j information.")
+        if not (ollama_base_url or openai_apikey):
+            st.write("Enter the Ollama URL or OpenAI API Key.")
+        if openai_apikey:
+            llm_name = "gpt-3.5"
+            os.environ['OPENAI_API_KEY'] = openai_apikey
+
+os.environ["NEO4J_URL"] = url
+
+logger = get_logger(__name__)
+
+embeddings, dimension = load_embedding_model(
+    embedding_model_name, config={"ollama_base_url": ollama_base_url}, logger=logger
+)
+
+
+class StreamHandler(BaseCallbackHandler):
+    def __init__(self, container, initial_text=""):
+        self.container = container
+        self.text = initial_text
+
+    def on_llm_new_token(self, token: str, **kwargs) -> None:
+        self.text += token
+        self.container.markdown(self.text)
+
+llm = load_llm(llm_name, logger=logger, config={"ollama_base_url": ollama_base_url})
+
+
+def main():
+        st.header("📄Chat with your pdf file")
+
+        # upload a your pdf file
+        pdf = st.file_uploader("Upload your PDF", type="pdf")
+
+        if pdf is not None:
+            pdf_reader = PdfReader(pdf)
+
+            text = ""
+            for page in pdf_reader.pages:
+                text += page.extract_text()
+
+            # langchain_textspliter
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=1000, chunk_overlap=200, length_function=len
+            )
+
+            chunks = text_splitter.split_text(text=text)
+
+            # Store the chunks part in db (vector)
+            vectorstore = Neo4jVector.from_texts(
+                chunks,
+                url=url,
+                username=username,
+                password=password,
+                embedding=embeddings,
+                index_name="pdf_bot",
+                node_label="PdfBotChunk",
+                pre_delete_collection=True,  # Delete existing PDF data
+            )
+            qa = RetrievalQA.from_chain_type(
+                llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever()
+            )
+
+            # Accept user questions/query
+            query = st.text_input("Ask questions about your PDF file")
+
+            if query:
+                stream_handler = StreamHandler(st.empty())
+                qa.run(query, callbacks=[stream_handler])
+
+
+if __name__ == "__main__":
+     main()