app.py

app.py

@@ -1,7 +1,48 @@
 import gradio as gr
+import os
+import getpass
 
-def greet(name):
-    return "Hello " + name + "!!"
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.vectorstores import DeepLake
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+from dotenv import load_dotenv
+
+load_dotenv()
+
+os.environ.get("ACTIVELOOP_TOKEN")
+username = "rihp" # replace with your username from app.activeloop.ai
+projectname = "polywrap5" # replace with your project name from app.activeloop.ai
+
+embeddings = OpenAIEmbeddings(disallowed_special=())
+
+db = DeepLake(dataset_path=f"hub://{username}/{projectname}", read_only=True, embedding_function=embeddings)
+
+retriever = db.as_retriever()
+retriever.search_kwargs['distance_metric'] = 'cos'
+retriever.search_kwargs['fetch_k'] = 100
+retriever.search_kwargs['maximal_marginal_relevance'] = True
+retriever.search_kwargs['k'] = 10
+
+
+model = ChatOpenAI(model_name='gpt-3.5-turbo') # switch to 'gpt-4'
+qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)
+
+
+def model(prompt):
+    questions = [
+         prompt
+    ] 
+    chat_history = []
+
+    for question in questions:  
+        result = qa({"question": question, "chat_history": chat_history})
+        chat_history.append((question, result['answer']))
+        print(f"-> **Question**: {question} \n")
+        print(f"**Answer**: {result['answer']} \n")
+    return result['answer']
+
+
+iface = gr.Interface(fn=model, inputs="text", outputs="text")
 iface.launch()