Create app.py

app.py

@@ -0,0 +1,97 @@
+import os
+import shutil
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import DirectoryLoader
+from langchain_openai import OpenAIEmbeddings
+from langchain.vectorstores.chroma import Chroma
+from langchain_openai import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+import gradio as gr
+
+script_directory = os.path.dirname(os.path.abspath(__file__))
+DATA_PATH = os.path.join(script_directory, "docs")
+CHROMA_PATH = "chroma"
+ 	  
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+	  
+PROMPT_TEMPLATE = """
+nswer the question based only on the following context:
+{context}
+---
+Answer the question based on the above context: {question}
+"""
+
+def load_documents():
+
+    loader = DirectoryLoader(DATA_PATH, glob="*.pdf")
+    documents = loader.load()
+    return documents
+
+def split_text(documents):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=300,
+        chunk_overlap=100,
+        length_function=len,
+        add_start_index=True,
+   )
+    chunks = text_splitter.split_documents(documents)
+    print(f"Split {len(documents)} documents into {len(chunks)} chunks.")
+    return chunks
+
+def save_to_chroma(chunks):
+    # Clear out the database first.
+    if os.path.exists(CHROMA_PATH):
+        shutil.rmtree(CHROMA_PATH)
+
+    embeddings = OpenAIEmbeddings()
+    # Create a new DB from the documents.
+    db = Chroma.from_documents(
+        chunks, embeddings, persist_directory=CHROMA_PATH
+   )
+    db.persist()
+    print(f"Saved {len(chunks)} chunks to {CHROMA_PATH}.")
+
+
+def get_response(query_text):
+  
+    # Prepare the DB.
+    embedding_function = OpenAIEmbeddings()
+    db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
+	  
+
+    results = db.similarity_search_with_relevance_scores(query_text, k=4)
+    if len(results) == 0 or results[0][1] < 0.7:
+        print(f"Unable to find matching results.")
+        return
+     
+    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
+ 	  
+    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
+    prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
+    prompt = prompt_template.format(context=context_text, question=query_text)
+	  
+    model = ChatOpenAI()
+    response_text = model.predict(prompt)
+	  
+    sources = [doc.metadata.get("source", None) for doc, _score in results]
+    formatted_response = f"Response: {response_text}\nSources: {sources}"
+    print(formatted_response)
+	  
+def chatbot(query_text):
+    documents = load_documents()
+    chunks = split_text(documents)
+    save_to_chroma(chunks)
+    get_response(query_text)
+    
+   
+   
+   	  
+
+iface = gr.Interface(fn=chatbot,
+        inputs=gr.components.Textbox(lines=7, label="Enter your text"),
+        outputs="text",
+        title="UK Insurance Law AI Tool")
+     
+
+
+iface.launch()