Create app.py

app.py

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+import gradio as gr
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.vectorstores import FAISS
+from langchain.document_loaders.csv_loader import CSVLoader
+
+def conversational_chat(query, lang_model_key, file_upload):
+    global chain, session_history
+    if file_upload is not None:
+        # Loading the CSV file
+        loader = CSVLoader(file_path=file_upload, encoding="utf-8")
+        data = loader.load()
+
+        # Initializing embeddings and vectors
+        embeddings = OpenAIEmbeddings(openai_api_key=lang_model_key)
+        vectors = FAISS.from_documents(data, embeddings)
+
+        # Creating the ConversationalRetrievalChain
+        chain = ConversationalRetrievalChain.from_llm(llm=ChatOpenAI(temperature=0.0, model_name='gpt-4', openai_api_key=lang_model_key),
+                                                      retriever=vectors.as_retriever())
+        session_history = []
+
+    result = chain({"question": query, "chat_history": session_history})
+    session_history.append((query, result["answer"]))
+    return result["answer"]
+
+iface = gr.Interface(fn=conversational_chat,
+                     inputs=[gr.Textbox(label="Query", lines=7),
+                             gr.Textbox(label="Your OpenAI API key:", type="password"),
+                             gr.File(label="Upload your CSV file:", type="binary")],
+                     outputs="text",
+                     title="Conversational CSV Chat: Please upload your file and set your API Key in order to use the functionalities",
+
+                     )
+iface.launch()