Add add to ds func

app.py

@@ -81,13 +81,42 @@ Your output should be in JSON FORMAT with the keys "REASONING" and "SCORE":
     lynx_time = round(time.time() - start_time, 2)  # Calculate time taken for Lynx
     return results, lynx_time
 # Function to judge reliability based on the selected input format
+
+
+async def add_to_dataset(category,document,question,answer,claim,conv_prefix,lynx_output,veritas_output):
+    conv_prefix = convert_to_message_array(conv_prefix)
+    dataset = load_dataset("collinear-ai/veritas-demo-dataset")
+    new_row = {
+        'style':category,
+    'document':document,
+    'question':question,
+    'answer':answer,
+    'claim':claim,
+    'conv_prefix':conv_prefix[:-1],
+    'response':conv_prefix[-1],
+    'lynx_output':lynx_output,
+    'veritas_output':veritas_output,
+        }
+    train_dataset = dataset['train']
+
+    df = train_dataset.to_pandas()
+    df2 = pd.DataFrame([new_row])
+    df = pd.concat([df, df2],ignore_index=True)
+
+    new_train_dataset = Dataset.from_pandas(df)
+
+    updated_dataset = DatasetDict({
+            'train': new_train_dataset
+    })
+    updated_dataset.push_to_hub("collinear-ai/veritas-demo-dataset",token=os.getenv("HF_TOKEN"))
+
 async def judge_reliability(input_style, document, conversation, claim, question, answer):
     start_time = time.time() 
     if input_style == "Dialog":
         print(conversation)
         conversation = convert_to_message_array(conversation=conversation)
         print(conversation)
-        outputs= await collinear.judge.veritas.conversation(document,conversation[:-1],conversation[-1])
+        outputs= await collinear.judge.veritas.conversation('72267aea-e1c7-4f38-8eb8-f5e3c2abc279',document,conversation[:-1],conversation[-1])
     elif input_style == "NLI":
         outputs = await collinear.judge.veritas.natural_language_inference(document,claim)
     elif input_style == "QA format":
@@ -214,8 +243,13 @@ assistant:Yes, it is about $38Bn.""")
         fn=lynx,
         inputs=[input_style_dropdown,document_input,question_input,answer_input],
         outputs=[lynx_output, lynx_time_output]
+    ).then(
+        fn=add_to_dataset, 
+        inputs=[input_style_dropdown,document_input,question_input,answer_input,claim_input,conversation_input,lynx_output,result_output],
+        outputs=[]
     )
 
+
 # Launch the demo
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1,4 +1,4 @@
 jinja2
 sentencepiece
 gradio
-collinear==0.1.24
+collinear==0.1.25