Update app.py

app.py

@@ -30,47 +30,8 @@ text_splitter = RecursiveCharacterTextSplitter(
 
 documents = text_splitter.split_documents(data)
 
-
 print("Got docs split")
-#Input openai api key
-# Check if the OpenAI API Key is empty
-def check_api_key(api_key):
-    if api_key == "":
-        return False
-    return True
-
-
-
-# Create the Gradio interface
-def gradio_interface(api_key):
-    if not check_api_key(api_key):
-        return "API key is empty. Please provide a valid API key."
-    else:
-        # Create the second page interface
-        question_input = gr.inputs.Textbox(label="Question")
-        answer_output = gr.outputs.Textbox(label="Answer")
-
-        # Function for the second page interface
-        def infer_question(question):
-            answer = make_inference(question)
-            return answer
-
-        # Define the second page of the Gradio interface
-        second_page = gr.Interface(fn=infer_question, inputs=question_input, outputs=answer_output, title="Ask me about Ted Lasso 📺⚽")
-
-        # Launch the second page interface
-        second_page.launch()
-
-# Define the input interface for the first page (OpenAI API Key entry)
-api_key_input = gr.inputs.Textbox(label="OpenAI API Key")
-
-# Create the first page interface
-first_page = gr.Interface(fn=gradio_interface, inputs=api_key_input, title="OpenAI API Key Entry")
-
-# Launch the first page interface
-first_page.launch()
 
-print("got API key")
 # Create the embeddings
 embeddings = OpenAIEmbeddings()
 #Load the model
@@ -85,10 +46,23 @@ print("Created retriever")
 #create the QA chain
 ted_lasso_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)
 # Function to make inferences and provide answers
-def make_inference(question):
-    # Perform inference using the question and return the answer
+def make_inference(query):
     print("reached inference")
-    return ted_lasso_qa.run(question)
+    return ted_lasso_qa.run(query)
+
+if __name__ == "__main__":
+    # make a gradio interface
+    import gradio as gr
+
+    gr.Interface(
+        make_inference,
+        [
+            gr.inputs.Textbox(lines=2, label="Query"),
+        ],
+        gr.outputs.Textbox(label="Response"),
+        title="Ask me about Ted Lasso 📺⚽"
+        description="Ask me about Ted Lasso 📺⚽ is a tool that allows you to ask questions the tv series Ted Lasso",
+    ).launch()