first

app.py

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+# for setting/extracting environment variables such as API keys
+import os
+
+### 1. For Web Scraping
+# for querying Financial Modelling Prep API
+from urllib.request import urlopen
+import json
+
+### 2. For Converting Scraped Text Into a Vector Store of Chunked Documents
+# for tokenizing texts and splitting them into chunks of documents
+from transformers import GPT2TokenizerFast
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+# for turning documents into embeddings before putting them in vector store
+from langchain.embeddings import HuggingFaceEmbeddings
+# for vector store for documents
+from langchain.vectorstores import Chroma
+
+### 3. For Querying LLM
+# for loading HuggingFace LLM models from the hub
+from langchain.llms import HuggingFaceHub
+# for querying LLM conveniently using the context
+from langchain.chains.question_answering import load_qa_chain
+
+### 4. For Gradio App UI
+import gradio as gr
+
+fmp_api_key = os.environ['FMP_API_KEY']
+
+
+def get_jsonparsed_data(url):
+    response = urlopen(url)
+    data = response.read().decode("utf-8")
+    return json.loads(data)
+
+# initialize the following tokenizers and splitters to tokenize and split the texts into chunks later (feel free to try others)
+tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
+text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=200, chunk_overlap=20)
+
+# initialize the default model for embedding the tokenized texts, the articles will be stored in this embedded form in the vector database
+hf_embeddings = HuggingFaceEmbeddings()
+
+# Load the huggingface inference endpoint of an LLM model
+# Name of the LLM model we are using, feel free to try others!
+model = "mistralai/Mistral-7B-Instruct-v0.1"
+
+# This is an inference endpoint API from huggingface, the model is not run locally, it is run on huggingface
+hf_llm = HuggingFaceHub(repo_id=model,model_kwargs={'temperature':0.5,"max_new_tokens":300})
+
+os.system("rm -r chromadb_earnings_transcripts_extracted")
+os.system("rm earnings_transcripts_chromadb.zip")
+os.system("wget https://github.com/damianboh/test_earnings_calls/raw/main/earnings_transcripts_chromadb.zip")
+os.system("unzip earnings_transcripts_chromadb.zip -d chromadb_earnings_transcripts_extracted")
+
+chroma_db = Chroma(persist_directory='chromadb_earnings_transcripts_extracted/chromadb_earnings_transcripts',embedding_function=hf_embeddings)
+
+
+
+def source_question_answer(query:str,vectorstore:Chroma=chroma_db,llm:HuggingFaceHub=hf_llm):
+  """
+  Return answer to the query
+  """
+  input_docs = vectorstore.similarity_search(query,k=4)
+  qa_chain = load_qa_chain(llm, chain_type="stuff")
+  query = f"[INST]According to the earnings calls transcripts earlier, {query}[INST]"
+
+  response = qa_chain.run(input_documents=input_docs, question=query)
+  source_docs_1 =  input_docs[0].page_content
+  source_docs_2 =  input_docs[1].page_content
+  source_docs_3 =  input_docs[2].page_content
+  source_docs_4 =  input_docs[3].page_content
+
+  source_title_1 = input_docs[0].metadata['title']
+  source_title_2 = input_docs[1].metadata['title']
+  source_title_3 = input_docs[2].metadata['title']
+  source_title_4 = input_docs[3].metadata['title']
+
+  return response,source_docs_1 ,source_docs_2,source_docs_3,source_docs_4, source_title_1, source_title_2, source_title_3, source_title_4
+
+
+with gr.Blocks() as app:
+
+    with gr.Row():
+        gr.HTML("<h1>Chat with Tesla 2023 Earnings Calls Transcripts</h1>")
+
+    with gr.Row():
+        query = gr.Textbox("Is Elon happy about Tesla?", placeholder="Enter question here...", label="Enter question")
+        btn = gr.Button("Ask Question")
+
+    with gr.Row():
+        gr.HTML("<h3>Answer</h3>")
+
+    with gr.Row():
+        answer = gr.Textbox(label="Answer")
+
+    with gr.Row():
+        gr.HTML("<h3>Sources Referenced from Tesla 2023 Earnings Calls Transcripts</h3>")
+
+    with gr.Row():
+        with gr.Column():
+          source_title_1 = gr.Markdown()
+          source1 = gr.Textbox(label="Source Text 1")
+        with gr.Column():
+          source_title_2 = gr.Markdown()
+          source2 = gr.Textbox(label="Source Text 2")
+
+    with gr.Row():
+        with gr.Column():
+          source_title_3 = gr.Markdown()
+          source3 = gr.Textbox(label="Source Text 3")
+        with gr.Column():
+          source_title_4 = gr.Markdown()
+          source4 = gr.Textbox(label="Source Text 4")
+   
+    query.submit(fn=source_question_answer, inputs=[query],
+              outputs=[answer, source1, source2, source3, source4, source_title_1, source_title_2, source_title_3, source_title_4])
+    
+    btn.click(fn=source_question_answer, inputs=[query],
+              outputs=[answer, source1, source2, source3, source4, source_title_1, source_title_2, source_title_3, source_title_4])
+
+app.launch(share=True, debug=True)

requirements.txt

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+gradio==3.45.2
+chromadb==0.4.13
+langchain==0.0.305
+transformers==4.33.3
+sentence-transformers==2.2.2