Spaces:
Running
Running
Abid Ali Awan
commited on
Commit
·
dbc2cc3
1
Parent(s):
c84dd0f
improve the text trigger function
Browse files
app.py
CHANGED
@@ -1,11 +1,12 @@
|
|
1 |
import os
|
|
|
2 |
import gradio as gr
|
|
|
3 |
from langchain_core.output_parsers import StrOutputParser
|
|
|
4 |
from langchain_core.runnables import RunnablePassthrough
|
5 |
from langchain_groq import ChatGroq
|
6 |
from langchain_huggingface import HuggingFaceEmbeddings
|
7 |
-
from langchain_chroma import Chroma
|
8 |
-
from langchain_core.prompts import PromptTemplate
|
9 |
|
10 |
# Load the API key from environment variables
|
11 |
groq_api_key = os.getenv("Groq_API_Key")
|
@@ -14,8 +15,9 @@ groq_api_key = os.getenv("Groq_API_Key")
|
|
14 |
llm = ChatGroq(model="llama-3.1-70b-versatile", api_key=groq_api_key)
|
15 |
|
16 |
# Initialize the embedding model
|
17 |
-
embed_model = HuggingFaceEmbeddings(
|
18 |
-
|
|
|
19 |
|
20 |
# Load the vector store from a local directory
|
21 |
vectorstore = Chroma(
|
@@ -48,6 +50,7 @@ rag_chain = (
|
|
48 |
| StrOutputParser()
|
49 |
)
|
50 |
|
|
|
51 |
# Define the function to stream the RAG memory
|
52 |
def rag_memory_stream(text):
|
53 |
partial_text = ""
|
@@ -56,6 +59,7 @@ def rag_memory_stream(text):
|
|
56 |
# Yield the updated conversation history
|
57 |
yield partial_text
|
58 |
|
|
|
59 |
# Set up the Gradio interface
|
60 |
title = "Real-time AI App with Groq API and LangChain"
|
61 |
description = """
|
@@ -68,15 +72,18 @@ demo = gr.Interface(
|
|
68 |
title=title,
|
69 |
description=description,
|
70 |
fn=rag_memory_stream,
|
71 |
-
inputs=
|
72 |
-
|
|
|
|
|
|
|
|
|
73 |
live=True,
|
74 |
batch=True,
|
75 |
max_batch_size=10000,
|
76 |
concurrency_limit=12,
|
77 |
allow_flagging="never",
|
78 |
theme=gr.themes.Soft(),
|
79 |
-
trigger_mode="always_last",
|
80 |
)
|
81 |
|
82 |
# Launch the Gradio interface
|
|
|
1 |
import os
|
2 |
+
|
3 |
import gradio as gr
|
4 |
+
from langchain_chroma import Chroma
|
5 |
from langchain_core.output_parsers import StrOutputParser
|
6 |
+
from langchain_core.prompts import PromptTemplate
|
7 |
from langchain_core.runnables import RunnablePassthrough
|
8 |
from langchain_groq import ChatGroq
|
9 |
from langchain_huggingface import HuggingFaceEmbeddings
|
|
|
|
|
10 |
|
11 |
# Load the API key from environment variables
|
12 |
groq_api_key = os.getenv("Groq_API_Key")
|
|
|
15 |
llm = ChatGroq(model="llama-3.1-70b-versatile", api_key=groq_api_key)
|
16 |
|
17 |
# Initialize the embedding model
|
18 |
+
embed_model = HuggingFaceEmbeddings(
|
19 |
+
model_name="mixedbread-ai/mxbai-embed-large-v1", model_kwargs={"device": "cpu"}
|
20 |
+
)
|
21 |
|
22 |
# Load the vector store from a local directory
|
23 |
vectorstore = Chroma(
|
|
|
50 |
| StrOutputParser()
|
51 |
)
|
52 |
|
53 |
+
|
54 |
# Define the function to stream the RAG memory
|
55 |
def rag_memory_stream(text):
|
56 |
partial_text = ""
|
|
|
59 |
# Yield the updated conversation history
|
60 |
yield partial_text
|
61 |
|
62 |
+
|
63 |
# Set up the Gradio interface
|
64 |
title = "Real-time AI App with Groq API and LangChain"
|
65 |
description = """
|
|
|
72 |
title=title,
|
73 |
description=description,
|
74 |
fn=rag_memory_stream,
|
75 |
+
inputs=gr.Textbox(
|
76 |
+
label="Enter your Star Wars question:",
|
77 |
+
trigger_mode="always_last",
|
78 |
+
default="Who is luke?",
|
79 |
+
),
|
80 |
+
outputs=gr.Textbox(label="Awnser:", default="...", trigger_mode="auto"),
|
81 |
live=True,
|
82 |
batch=True,
|
83 |
max_batch_size=10000,
|
84 |
concurrency_limit=12,
|
85 |
allow_flagging="never",
|
86 |
theme=gr.themes.Soft(),
|
|
|
87 |
)
|
88 |
|
89 |
# Launch the Gradio interface
|