Update app.py

app.py

@@ -1,13 +1,13 @@
 import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-from jinja2 import Template
-import torch
-import json
+# from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+# from jinja2 import Template
+# import torch
+# import json
 
-# load the judge
-device = "cuda:0"
-model_name = "collinear-ai/collinear-reliability-judge-v8-sauber"
-model_pipeline = pipeline(task="text-classification", model=model_name, device=device)
+# # load the judge
+# device = "cuda:0"
+# model_name = "collinear-ai/collinear-reliability-judge-v8-sauber"
+# model_pipeline = pipeline(task="text-classification", model=model_name, device=device)
 
 # templates
 conv_template = Template(
@@ -46,30 +46,30 @@ nli_template = Template(
 
 # Function to dynamically update inputs based on the input style
 def update_inputs(input_style):
-    if input_style == "Conv":
-        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-    elif input_style == "NLI":
-        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
-    elif input_style == "QA format":
-        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)
+    # if input_style == "Conv":
+    #     return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+    # elif input_style == "NLI":
+    #     return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
+    # elif input_style == "QA format":
+    #     return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)
 
 
 # Function to judge reliability based on the selected input format
 def judge_reliability(input_style, document, conversation, claim, question, answer):
-    with torch.no_grad():
-        if input_style == "Conv":
-            conversation = json.loads(conversation)
-            text = conv_template.render(document=document, conversation=conversation)
-        elif input_style == "NLI":
-            text = nli_template.render(document=document, claim=claim)
-        elif input_style == "QA format":
-            text = qa_template.render(document=document, question=question, answer=answer)
-
-        print(text)
+    # with torch.no_grad():
+    #     if input_style == "Conv":
+    #         conversation = json.loads(conversation)
+    #         text = conv_template.render(document=document, conversation=conversation)
+    #     elif input_style == "NLI":
+    #         text = nli_template.render(document=document, claim=claim)
+    #     elif input_style == "QA format":
+    #         text = qa_template.render(document=document, question=question, answer=answer)
+
+    #     print(text)
         
-        outputs = model_pipeline(text)
-        results = f"Reliability Judge Outputs: {outputs}"
-    return results
+    #     outputs = model_pipeline(text)
+    #     results = f"Reliability Judge Outputs: {outputs}"
+    # return results