Upload app.py

app.py

@@ -87,7 +87,7 @@ Here are some documents that are relevant to the question mentioned below.
 def predict(user_input,company):
     sample = {
     'user_input':user_input,
-    'company':company
+    'company':company_input
     }
 
     filter = "dataset/"+company+"-10-k-2023.pdf"
@@ -122,7 +122,7 @@ def predict(user_input,company):
 
 
     # Get response from the LLM
-    prediction = response.choices[0].message.content.strip()
+    
 
     # While the prediction is made, log both the inputs and outputs to a local log file
     # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
@@ -148,7 +148,7 @@ company_input = gr.Radio(
     label = 'company'
     )
 
-model_output = gr.Textbox (label = 'Response')
+model_output = gr.label (label = 'Response')
 
 # Add text box and radio button to the interface
 # The radio button is used to select the company 10k report in which the context needs to be retrieved.
@@ -161,7 +161,7 @@ model_output = gr.Textbox (label = 'Response')
 demo = gr.Interface(
     fn=predict,
     inputs=[user_input,company_input],
-    outputs=prediction,
+    outputs=model_output,
     title="RAG on 10k-reports",
     description="This API allows you to query on annaul reports",
     concurrency_limit=16