Upload app.py

app.py

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+##Setup
+
+#Import the necessary Libraries
+import os
+import uuid
+import json
+import gradio as gr
+from openai import OpenAI
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain_community.vectorstores import Chroma
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+# Create Client
+anyscale_api_key = userdata.get('anyscale_api_key')  # Ensure to set this environment variable
+
+client = OpenAI(
+    base_url="https://api.endpoints.anyscale.com/v1",
+    api_key=anyscale_api_key
+)
+
+# Define the embedding model and the vectorstore
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
+
+# Load the persisted vectorDB
+collection_name = 'finsights-grey-10k-2023'
+
+vectorstore_persisted = Chroma(
+    collection_name=collection_name,
+    embedding_function=embedding_model,
+    persist_directory='/content/finsightsgrey_db'
+)
+
+retriever = vectorstore_persisted.as_retriever(
+    search_type="similarity",
+    search_kwargs={'k': 5},
+)
+
+# Prepare the logging functionality
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+# Define the Q&A system message
+qna_system_message = """
+You are an assistant to a financial technology firm who answers user queries on 10-K reports from various industry players which contain detailed information about financial performance, risk factors, market trends, and strategic initiatives.
+User input will have the context required by you to answer user questions.
+This context will begin with the token: ###Context.
+
+When crafting your response,select the most relevant context or contexts to answer the question.
+
+User questions will begin with the token: ###Question.
+
+Please answer only using the context provided in the input. Do not mention anything about the context in your final answer.
+
+If the answer is not found in the context, respond "I don't know".
+"""
+
+# Define the user message template
+qna_user_message_template = """
+###Context
+Here are some documents that are relevant to the question mentioned below.-
+{context}
+
+###Question
+{question}
+"""
+
+# Define the predict function that runs when 'Submit' is clicked or when an API request is made
+def predict(user_input, company):
+    filter = {"source": f"/content/dataset/{company}-10-k-2023.pdf"}
+    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter=filter)
+    
+    # Create context_for_query
+    context_list = [f"Page {doc.metadata['page']}: {doc.page_content}" for doc in relevant_document_chunks]
+    context_for_query = ".".join(context_list)
+
+    # Create messages
+    prompt = [
+        {'role': 'system', 'content': qna_system_message},
+        {'role': 'user', 'content': qna_user_message_template.format(context=context_for_query, question=user_input)}
+    ]
+
+    # Get response from the LLM
+    try:
+        response = client.chat.completions.create(
+            model="mlabonne/NeuralHermes-2.5-Mistral-7B",
+            messages=prompt,
+            temperature=0
+        )
+        prediction = response.choices[0].message.content
+    except Exception as e:
+        prediction = f'Sorry, I encountered the following error: \n {e}'
+
+    print(prediction)
+
+    # Log both the inputs and outputs to a local log file
+    #with scheduler.lock:
+        #with log_file.open("a") as f:
+            #f.write(json.dumps({
+                #'user_input': user_input,
+                #'retrieved_context': context_for_query,
+                #'model_response': prediction
+            #}))
+            #f.write("\n")
+
+    return prediction
+
+# Set-up the Gradio UI
+# Add text box and radio button to the interface
+# The radio button is used to select the company 10k report in which the context needs to be retrieved.
+textbox = gr.Textbox(placeholder="Enter your query here")
+company = gr.Radio(choices=["IBM", "META", "aws", "google", "msft"], label="Company")
+
+# Create the interface
+demo = gr.Interface(
+    inputs=[textbox, company],
+    fn=predict,
+    outputs="text",
+    description="This web API presents an interface to ask questions on contents of IBM, META, AWS, GOOGLE and MSFT 10-K reports for the year 2023",
+    article="Note that questions that are not relevant to the aforementioned companies' 10-K reports will not be answered",
+    title="Q&A for IBM, META, AWS, GOOG & MSFT 10-K Statements",
+    examples=[
+        ["Has the company made any significant acquisitions in the AI space, and how are these acquisitions being integrated into the company's strategy?", "IBM"],
+        ["How much capital has been allocated towards AI research and development?", "META"],
+        ["What initiatives has the company implemented to address ethical concerns surrounding AI, such as fairness, accountability, and privacy?", "aws"],
+        ["How does the company plan to differentiate itself in the AI space relative to competitors?", "google"]
+    ],
+    concurrency_limit=16
+)
+
+demo.queue()
+demo.launch(share=True, debug=False)