Update app.py

app.py

@@ -1,143 +1,159 @@
-# Import the necessary Libraries
-import json
-import uuid
-import os
-
-from openai import OpenAI
-import gradio as gr
-
-from langchain_community.embeddings.sentence_transformer import (
-    SentenceTransformerEmbeddings
-)
-from langchain_community.vectorstores import Chroma
-from huggingface_hub import CommitScheduler
-from pathlib import Path
-
-
-client = OpenAI(
-    base_url="https://api.endpoints.anyscale.com/v1",
-    api_key=os.environ['ANYSCALE_API_KEY']
-)
-
-embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
-
-streamlit_collection = 'reports_collection'
-
-vectorstore_persisted = Chroma(
-    collection_name=streamlit_collection,
-    persist_directory='./reports_db',
-    embedding_function=embedding_model
-)
-
-# Prepare the logging functionality
-
-log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
-log_folder = log_file.parent
-
-scheduler = CommitScheduler(
-    repo_id="reports-qna",
-    repo_type="dataset",
-    folder_path=log_folder,
-    path_in_repo="data",
-    every=2
-)
-
-qna_system_message = """
-You are an assistant to a Financial Analyst. Your task is to summarize and provide relevant information to the financial analyst's question based on the provided context.
-
-User input will include the necessary context for you to answer their questions. This context will begin with the token: ###Context.
-The context contains references to specific portions of documents relevant to the user's query, along with page number from the report.
-The source for the context will begin with the token ###Page
-
-When crafting your response:
-1. Select only context relevant to answer the question.
-2. Include the source links in your response.
-3. User questions will begin with the token: ###Question.
-4. If the question is irrelevant or if you do not have the information to  respond with - "Sorry, this is out of my knowledge base"
-
-Please adhere to the following guidelines:
-- Your response should only be about the question asked and nothing else.
-- Answer only using the context provided.
-- Do not mention anything about the context in your final answer.
-- If the answer is not found in the context, it is very very important for you to respond with "Sorry, this is out of my knowledge base"
-- Always quote the page number when you use the context. Cite the relevant page number at the end of your response under the section - Page:
-- Do not make up sources Use the links provided in the sources section of the context and nothing else. You are prohibited from providing other links/sources.
-
-Here is an example of how to structure your response:
-
-Answer:
-[Answer]
-
-Page:
-[Page number]
-"""
-
-qna_user_message_template = """
-###Context
-Here are some documents and their page number that are relevant to the question mentioned below.
-{context}
-
-###Question
-{question}
-"""
-
-# Define the predict function that runs when 'Submit' is clicked or when a API request is made
-def predict(user_input,company):
-
-    filter = "dataset/"+company+"-10-k-2023.pdf"
-    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source":filter})
-    context_list = [d.page_content + "\n ###Page: " + str(d.metadata['page']) + "\n\n " for d in relevant_document_chunks]
-    context_for_query = ".".join(context_list)
-    
-    prompt = [
-        {'role':'system', 'content': qna_system_message},
-        {'role': 'user', 'content': qna_user_message_template.format(
-            context=context_for_query,
-            question=user_input
-            )
-        }
-    ]
-
-    try:
-        response = client.chat.completions.create(
-            model='mistralai/Mixtral-8x7B-Instruct-v0.1',
-            messages=prompt,
-            temperature=0
-        )
-
-        prediction = response.choices[0].message.content
-
-    except Exception as e:
-        prediction = e
-
-    # While the prediction is made, log both the inputs and outputs to a local log file
-    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
-    # access
-    
-    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
-
-
-textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
-company = gr.Radio(choices=["google", "msft", "aws", "ibm", "meta"], label="Select the company")
-
-# Create the interface
-demo = gr.Interface(
-    inputs=[textbox, company], fn=predict, outputs="text",
-    title="10-k Reports Q&A System",
-    description="This web API presents an interface to ask questions on 10-k reports ",
-    concurrency_limit=16
-)
-
-demo.queue()
+# Import the necessary Libraries
+import json
+import uuid
+import os
+
+from openai import OpenAI
+import gradio as gr
+
+from langchain_community.embeddings.sentence_transformer import (
+    SentenceTransformerEmbeddings
+)
+from langchain_community.vectorstores import Chroma
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+
+import os
+
+os.environ['OPENAI_API_KEY']  = "gl-U2FsdGVkX18e2Pmna5tn6g6u7mqi55sN7xcOMntKGypQnR3Y4CQK5VfbJYc0Nt7c"
+
+os.environ["OPENAI_BASE_URL"] = "https://aibe.mygreatlearning.com/openai/v1"
+
+
+
+
+
+
+
+# Create Client
+
+client = OpenAI()
+
+
+
+
+model_name = 'gpt-4o-mini'
+
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
+
+streamlit_collection = 'reports_collection'
+
+vectorstore_persisted = Chroma(
+    collection_name=streamlit_collection,
+    persist_directory='./reports_db',
+    embedding_function=embedding_model
+)
+
+# Prepare the logging functionality
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="reports-qna",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+qna_system_message = """
+You are an assistant to a Financial Analyst. Your task is to summarize and provide relevant information to the financial analyst's question based on the provided context.
+
+User input will include the necessary context for you to answer their questions. This context will begin with the token: ###Context.
+The context contains references to specific portions of documents relevant to the user's query, along with page number from the report.
+The source for the context will begin with the token ###Page
+
+When crafting your response:
+1. Select only context relevant to answer the question.
+2. Include the source links in your response.
+3. User questions will begin with the token: ###Question.
+4. If the question is irrelevant or if you do not have the information to  respond with - "Sorry, this is out of my knowledge base"
+
+Please adhere to the following guidelines:
+- Your response should only be about the question asked and nothing else.
+- Answer only using the context provided.
+- Do not mention anything about the context in your final answer.
+- If the answer is not found in the context, it is very very important for you to respond with "Sorry, this is out of my knowledge base"
+- Always quote the page number when you use the context. Cite the relevant page number at the end of your response under the section - Page:
+- Do not make up sources Use the links provided in the sources section of the context and nothing else. You are prohibited from providing other links/sources.
+
+Here is an example of how to structure your response:
+
+Answer:
+[Answer]
+
+Page:
+[Page number]
+"""
+
+qna_user_message_template = """
+###Context
+Here are some documents and their page number that are relevant to the question mentioned below.
+{context}
+
+###Question
+{question}
+"""
+
+# Define the predict function that runs when 'Submit' is clicked or when a API request is made
+def predict(user_input,company):
+
+    filter = "dataset/"+company+"-10-k-2023.pdf"
+    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source":filter})
+    context_list = [d.page_content + "\n ###Page: " + str(d.metadata['page']) + "\n\n " for d in relevant_document_chunks]
+    context_for_query = ".".join(context_list)
+    
+    prompt = [
+        {'role':'system', 'content': qna_system_message},
+        {'role': 'user', 'content': qna_user_message_template.format(
+            context=context_for_query,
+            question=user_input
+            )
+        }
+    ]
+
+    try:
+        response = client.chat.completions.create(
+            model='mistralai/Mixtral-8x7B-Instruct-v0.1',
+            messages=prompt,
+            temperature=0
+        )
+
+        prediction = response.choices[0].message.content
+
+    except Exception as e:
+        prediction = e
+
+    # While the prediction is made, log both the inputs and outputs to a local log file
+    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
+    # access
+    
+    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
+
+
+textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
+company = gr.Radio(choices=["google", "msft", "aws", "ibm", "meta"], label="Select the company")
+
+# Create the interface
+demo = gr.Interface(
+    inputs=[textbox, company], fn=predict, outputs="text",
+    title="10-k Reports Q&A System",
+    description="This web API presents an interface to ask questions on 10-k reports ",
+    concurrency_limit=16
+)
+
+demo.queue()
 demo.launch()