Create app.py

app.py

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+import os, gradio
+from langchain.document_loaders import UnstructuredPDFLoader
+from langchain.indexes import VectorstoreIndexCreator
+from vectorstore import VectorstoreIndexCreator
+
+os.environ['OPENAI_API_KEY'] = os.getenv("OPENAI_API_KEY")
+
+text_folder = '10K_Annual_Reports'
+loaders = [UnstructuredPDFLoader(os.path.join(text_folder, fn)) for fn in os.listdir(text_folder)]
+
+# Create the index, if it does not exist, and save it
+if not os.path.isfile('VectorStoreIndex/chroma-embeddings.parquet'):
+  from langchain.vectorstores import Chroma
+  index = VectorstoreIndexCreator(vectorstore_cls=Chroma, vectorstore_kwargs={ "persist_directory": "VectorStoreIndex/"}).from_loaders(loaders)
+  index.vectorstore.persist()
+
+# Load the saved index
+index_saved = VectorstoreIndexCreator().from_persistent_index("VectorStoreIndex/")
+
+description = """This is an AI conversational agent where you provide it with the annual reports of companies, and it can study it and answer any questions 
+you have about it. Currently, the LLM has been trained on the following companies' 10-K reports: Amazon, Apple, Alphabet (Google), Meta (Facebook), Microsoft, 
+Netflix and Tesla.' I plan to include more companies' 10-K reports in future.
+
+Once the LLM is trained on a new 10-K report, it stores the vector embeddings of the document locally using ChromaDB to make the querying faster and also to 
+save time and money on creating the vector embeddings for the same document in future.
+
+The LLM's universe is only the 10-K reports it has been trained on; it cannot pull information from the internet. So, you can ask it about anything that's 
+contained in their 10-K reports. If it cannot find an answer to your query within the 10-K reports, it will reply with "I don't know". Some example of questions 
+you can ask are: 
+
+    - What are the risks for Tesla?
+    - What was Google's earnings for the last fiscal year?
+    - Who are the competetors of Apple?
+
+An example of querying about something the LLM's training did not include: 
+
+    - Query:    "What is Tesco?"
+    - Response: " Tesco is not mentioned in the context, so I don't know."
+"""
+
+def chat_response(query):
+  return index_saved.query(query)
+
+interface = gradio.Interface(fn=chat_response, inputs="text", outputs="text", title='Annual Reports GPT', description=description)
+
+interface.launch() #server_name="0.0.0.0", server_port=8080, share=True)