Create app.py

app.py

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+import os 
+import openai
+from dotenv import load_dotenv
+
+
+API_KEY = "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"
+
+# Write the API key to the .env file
+with open(".env", "w") as env_file:
+    env_file.write(f"openai_api_key={API_KEY}\n")
+    
+#echo openai_api_key="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" > .env
+
+load_dotenv(".env")
+
+openai.api_key = os.environ.get("openai_api_key")
+
+os.environ["OPENAI_API_KEY"] = openai.api_key
+
+with open("sample_awards.csv") as f:
+    sample_awards = f.read()
+
+from langchain.text_splitter import CharacterTextSplitter
+
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = "\n")
+texts = text_splitter.split_text(sample_awards)
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+
+embeddings = OpenAIEmbeddings()
+
+from langchain.vectorstores import Chroma
+
+docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
+
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+
+def question(text):
+    query = text
+    docs = docsearch.get_relevant_documents(query)
+    return chain.run(input_documents=docs, question=query)
+
+import gradio as gr
+
+gr.Interface(
+    question,
+    inputs="text",
+    outputs="text",
+    title="Awards Question Answering",
+).launch()