🌘w🌖

app.py

@@ -53,7 +53,7 @@ def prepare_prompt(query, retrieved_examples):
     return prompt, (titles, urls)
 
 
-@spaces.GPU
+# @spaces.GPU
 def talk(message, history):
     retrieved_examples = search(message)
     message, metadata = prepare_prompt(message, retrieved_examples)
@@ -92,42 +92,45 @@ def talk(message, history):
     partial_text = ""
     for new_text in streamer:
         partial_text += new_text
-        print(partial_text)
+        print("partial_text : ", partial_text)
         yield partial_text
     # partial_text += resources
     # yield partial_text
 
 
-TITLE = "RAG"
+TITLE = "# RAG"
 
 DESCRIPTION = """
 A rag pipeline with a chatbot feature
 
 Resources used to build this project :
 
-embedding model : https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1
-dataset : https://huggingface.co/datasets/not-lain/wikipedia-small-3000-embedded (used mxbai-colbert-large-v1 to create the embedding column )
-faiss docs : https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index 
-chatbot : https://huggingface.co/google/gemma-7b-it
+* embedding model : https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1
+* dataset : https://huggingface.co/datasets/not-lain/wikipedia-small-3000-embedded (used mxbai-colbert-large-v1 to create the embedding column )
+* faiss docs : https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index 
+* chatbot : https://huggingface.co/google/gemma-7b-it
 
 If you want to support my work please click on the heart react button ❤️🤗
 
 <sub><sup><sub><sup>psst, I am still open for work, so please reach me out at https://not-lain.github.io/</sup></sub></sup></sub>
 """
 
-demo = gr.ChatInterface(
-    fn=talk,
-    chatbot=gr.Chatbot(
-        show_label=True,
-        show_share_button=True,
-        show_copy_button=True,
-        likeable=True,
-        layout="bubble",
-        bubble_full_width=False,
-    ),
-    theme="Soft",
-    examples=[["what is machine learning"]],
-    title=TITLE,
-    description=DESCRIPTION,
-)
-demo.launch()
+
+with gr.Blocks() as demo:
+    gr.Markdown(TITLE)
+    gr.Markdown(DESCRIPTION)
+    gr.ChatInterface(
+        fn=talk,
+        chatbot=gr.Chatbot(
+            show_label=True,
+            show_share_button=True,
+            show_copy_button=True,
+            likeable=True,
+            layout="bubble",
+            bubble_full_width=False,
+        ),
+        theme="Soft",
+        examples=[["what is machine learning"]],
+    )
+
+demo.launch(debug=True)