Upload 4 files

app.py

@@ -1,15 +1,38 @@
 import gradio as gr
-from confidence import run_nli
+#from confidence import run_nli
 
+DESCRIPTION = """\
+# Llama-2 13B Chat
+This Space demonstrates model [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat) by Meta, a Llama 2 model with 13B parameters fine-tuned for chat instructions. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).
+🔎 For more details about the Llama 2 family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/llama2).
+🔨 Looking for an even more powerful model? Check out the large [**70B** model demo](https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI).
+🐇 For a smaller model that you can run on many GPUs, check our [7B model demo](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat).
+"""
 
-def greet(query):
-    results = run_nli(query, sample_size=3)
-    return results
+def greet(query, history):
+    #results = run_nli(query, sample_size=3)
+    #return results
+    return "this is the result"
 
 
 sample_list = [
-    "Tell me something about Albert Einstein, e.g., birth date and place and short bio ",
+    "Tell me something about Albert Einstein, e.g., a short bio with birth date and birth place",
+    "Tell me something about Lihu Chen, e.g., a short bio with birth date and birth place",
 ]
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text", examples=sample_list, cache_examples=True)
+iface = gr.ChatInterface(
+    fn=greet,
+    stop_btn=None,
+    # inputs="text",
+    # outputs="text",
+    examples=sample_list,
+    cache_examples=True
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    iface.render()
+    #gr.Markdown(LICENSE)
+
+
 iface.launch()

self_check_gpt.py

@@ -13,5 +13,5 @@ def nli_confidence(proofs, sentences):
         sampled_passages = proofs, # list of sampled passages
     )
     #print(sent_scores_nli)
-    sent_scores_nli = [1-s for s in sent_scores_nli]
+    sent_scores_nli = [round(1-s, 4) for s in sent_scores_nli]
     return sent_scores_nli