Update app.py

app.py

@@ -1,132 +1,131 @@
-import os
-import uuid
-import json
-
-import gradio as gr
-
-from openai import OpenAI
-pip install --upgrade openai
-
-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://aibe.mygreatlearning.com/openai/v1",
-    api_key="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"
-)
-
-embedding_model = SentenceTransformerEmbeddings(model_name='gpt=thenlper/gte-small')
-
-tesla_10k_collection = 'tesla-10k-2019-to-2023'
-
-vectorstore_persisted = Chroma(
-    collection_name=tesla_10k_collection,
-    persist_directory='./tesla_db',
-    embedding_function=embedding_model
-)
-
-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
-
-scheduler = CommitScheduler(
-    repo_id="document-qna-chroma-anyscale-logs",
-    repo_type="dataset",
-    folder_path=log_folder,
-    path_in_repo="data",
-    every=2
-)
-
-qna_system_message = """
-You are an assistant to a financial services firm who answers user queries on annual reports.
-Users will ask questions delimited by triple backticks, that is, ```.
-User input will have the context required by you to answer user questions.
-This context will begin with the token: ###Context.
-The context contains references to specific portions of a document relevant to the user query.
-Please answer only using the context provided in the input. However, do not mention anything about the context in your answer. 
-If the answer is not found in the context, respond "I don't know".
-"""
-
-qna_user_message_template = """
-###Context
-Here are some documents that are relevant to the question.
-{context}
-```
-{question}
-```
-"""
-
-# Define the predict function that runs when 'Submit' is clicked or when a API request is made
-def predict(user_input):
-
-    relevant_document_chunks = retriever.invoke(user_input)
-    context_list = [d.page_content 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='mlabonne/NeuralHermes-2.5-Mistral-7B',
-            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)
-
-# Create the interface
-demo = gr.Interface(
-    inputs=textbox, fn=predict, outputs="text",
-    title="AMA on Tesla 10-K statements",
-    description="This web API presents an interface to ask questions on contents of the Tesla 10-K reports for the period 2019 - 2023.",
-    article="Note that questions that are not relevant to the Tesla 10-K report will not be answered.",
-    examples=[["What was the total revenue of the company in 2022?", "$ 81.46 Billion"],
-              ["Summarize the Management Discussion and Analysis section of the 2021 report in 50 words.", ""],
-              ["What was the company's debt level in 2020?", ""],
-              ["Identify 5 key risks identified in the 2019 10k report? Respond with bullet point summaries.", ""]
-             ],
-    concurrency_limit=16
-)
-
-demo.queue()
+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
+
+
+client = OpenAI(
+    #base_url="https://aibe.mygreatlearning.com/openai/v1",
+    api_key="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"
+)
+
+embedding_model = SentenceTransformerEmbeddings(model_name='gpt=thenlper/gte-small')
+
+tesla_10k_collection = 'tesla-10k-2019-to-2023'
+
+vectorstore_persisted = Chroma(
+    collection_name=tesla_10k_collection,
+    persist_directory='./tesla_db',
+    embedding_function=embedding_model
+)
+
+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
+
+scheduler = CommitScheduler(
+    repo_id="document-qna-chroma-anyscale-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+qna_system_message = """
+You are an assistant to a financial services firm who answers user queries on annual reports.
+Users will ask questions delimited by triple backticks, that is, ```.
+User input will have the context required by you to answer user questions.
+This context will begin with the token: ###Context.
+The context contains references to specific portions of a document relevant to the user query.
+Please answer only using the context provided in the input. However, do not mention anything about the context in your answer. 
+If the answer is not found in the context, respond "I don't know".
+"""
+
+qna_user_message_template = """
+###Context
+Here are some documents that are relevant to the question.
+{context}
+```
+{question}
+```
+"""
+
+# Define the predict function that runs when 'Submit' is clicked or when a API request is made
+def predict(user_input):
+
+    relevant_document_chunks = retriever.invoke(user_input)
+    context_list = [d.page_content 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='mlabonne/NeuralHermes-2.5-Mistral-7B',
+            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)
+
+# Create the interface
+demo = gr.Interface(
+    inputs=textbox, fn=predict, outputs="text",
+    title="AMA on Tesla 10-K statements",
+    description="This web API presents an interface to ask questions on contents of the Tesla 10-K reports for the period 2019 - 2023.",
+    article="Note that questions that are not relevant to the Tesla 10-K report will not be answered.",
+    examples=[["What was the total revenue of the company in 2022?", "$ 81.46 Billion"],
+              ["Summarize the Management Discussion and Analysis section of the 2021 report in 50 words.", ""],
+              ["What was the company's debt level in 2020?", ""],
+              ["Identify 5 key risks identified in the 2019 10k report? Respond with bullet point summaries.", ""]
+             ],
+    concurrency_limit=16
+)
+
+demo.queue()
 demo.launch()