Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import os
 from langchain_community.document_loaders import JSONLoader
 from langchain_community.vectorstores import Qdrant
-from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings
 from sentence_transformers.cross_encoder import CrossEncoder
 from groq import Groq
 
@@ -28,7 +28,6 @@ def reranking_results(query, top_k_results, rerank_model):
     return reranked_results
 
 
-json_path = "format_food.json"
 loader = JSONLoader(
     file_path=json_path,
     jq_schema='.dishes[].dish',
@@ -36,20 +35,29 @@ loader = JSONLoader(
     content_key='doc',
     metadata_func=metadata_func
 )
+
 data = loader.load()
 
 # Models
-model_name = "Snowflake/snowflake-arctic-embed-xs"
+# model_name = "Snowflake/snowflake-arctic-embed-xs"
 # rerank_model = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1")
 
 # Embedding
+# model_kwargs = {"device": "cpu"}
+# encode_kwargs = {"normalize_embeddings": True}
+# hf_embedding = HuggingFaceEmbeddings(
+#     model_name=model_name, 
+#     encode_kwargs=encode_kwargs, 
+#     model_kwargs=model_kwargs,
+#     show_progress=True
+# )
+model_name = "BAAI/bge-small-en"
 model_kwargs = {"device": "cpu"}
 encode_kwargs = {"normalize_embeddings": True}
-hf_embedding = HuggingFaceEmbeddings(
+hf_embedding = HuggingFaceBgeEmbeddings(
     model_name=model_name, 
-    encode_kwargs=encode_kwargs, 
-    model_kwargs=model_kwargs,
-    show_progress=True
+    model_kwargs=model_kwargs, 
+    encode_kwargs=encode_kwargs
 )
 
 qdrant = Qdrant.from_documents(
@@ -64,32 +72,36 @@ def format_to_markdown(response_list):
     temp_string = "\n- ".join(response_list)
     return temp_string
 
-def run_query(query):
+def run_query(query: str, groq: bool):
     print("Running Query")
     answer = qdrant.similarity_search(query=query, k=10)
     title_and_description = f"# Best Choice:\nA {answer[0].metadata['title']}: {answer[0].page_content}"
     instructions = format_to_markdown(answer[0].metadata['instructions'])
     recipe = f"# Standard Method\n## Cooking time:\n{answer[0].metadata['time']}\n\n## Recipe:\n{instructions}"
     print("Returning query")
-    chat_completion = client.chat.completions.create(
-        messages=[
-           {
-                "role": "user",
-                "content": f"please write a more detailed recipe for the following recipe:\n{recipe}\n\n please return it in the same format.",
-            }
-        ],
-        model="Llama3-70b-8192",
-    )
-    groq_update = "# Groq Update\n"+chat_completion.choices[0].message.content
+    if groq:
+        chat_completion = client.chat.completions.create(
+            messages=[
+            {
+                    "role": "user",
+                    "content": f"please write a more detailed recipe for the following recipe:\n{recipe}\n\n please return it in the same format.",
+                }
+            ],
+            model="Llama3-70b-8192",
+        )
+        groq_update = "# Groq Update\n"+chat_completion.choices[0].message.content
+    else:
+        groq_update = "# Groq Update \nPlease select the tick box if you need more information."
     return title_and_description, recipe, groq_update
 
 with gr.Blocks() as demo:
     gr.Markdown("Start typing below and then click **Run** to see the output.")
     inp = gr.Textbox(placeholder="What sort of meal are you after?")
+    groq_button = gr.Checkbox(value=False, label="Use Llama for a better recipe?")
     title_output = gr.Markdown(label="Title and description")
     instructions_output = gr.Markdown(label="Recipe")
     updated_recipe = gr.Markdown(label="Updated Recipe")
     btn = gr.Button("Run")
-    btn.click(fn=run_query, inputs=inp, outputs=[title_output, instructions_output, updated_recipe])
+    btn.click(fn=run_query, inputs=[inp, groq_button], outputs=[title_output, instructions_output, updated_recipe])
 
 demo.launch()