Create app.py

app.py

@@ -0,0 +1,81 @@
+import os
+import gradio as gr
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.schema import AIMessage, HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+
+MAX_TOKENS = os.getenv("MAX_TOKENS")
+if MAX_TOKENS is None:
+    MAX_TOKENS = "8192"
+
+MAX_TOKENS = int(MAX_TOKENS)
+
+VERSION = "v1.3.1"
+AI_NAME = "Qbot"
+
+TEMPLATES = [
+    (
+        "Q&A", """
+Answer the following questions as best you can.
+Question: {input}
+Answer: Respond in the same language as the question.
+"""
+    ),
+    (
+        "Summarization", """
+Summarize the following text into one sentence. 
+text: {input}
+"""
+    ),
+]
+
+GROQ_MODELS = [
+    "llama3-70b-8192",
+    "mixtral-8x7b-32768",
+    "gemma-7b-it",
+    "llama3-8b-8192",
+]
+
+
+async def predict(message, history, model_name, template, temperature, max_tokens):
+    llm = ChatGroq(model_name=model_name, temperature=temperature,
+                   streaming=True, max_tokens=max_tokens)
+    langchain_history = []
+    for human, ai in history:
+        langchain_history.append(HumanMessage(content=human))
+        langchain_history.append(AIMessage(content=ai))
+    prompt_template = ChatPromptTemplate.from_messages(
+        langchain_history + [("human", template)])
+
+    chain = prompt_template | llm | StrOutputParser()
+    msg = ""
+    async for chunk in chain.astream({"input": message}):
+        msg = msg + chunk
+        yield msg
+
+
+demo = gr.ChatInterface(
+    fn=predict,
+    description="A Gradio chatbot powered by GroqCloud and Langchain " + VERSION,
+    additional_inputs_accordion=gr.Accordion(
+        label="Parameters", render=False, open=False),
+    additional_inputs=[
+        gr.Dropdown(label="Model", choices=GROQ_MODELS, value=GROQ_MODELS[0]),
+        gr.Dropdown(label="Prompt template",
+                    choices=TEMPLATES, value=TEMPLATES[0][1]),
+        gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, step=0.1,
+                  value=0.6),
+        gr.Number(label="Max Tokens", value=MAX_TOKENS,
+                  step=1024, minimum=1024, maximum=MAX_TOKENS),
+    ],
+    examples=[
+        ["What is Decision Tree Regression"],
+        ["Wite a love story with about 10000 words."],
+        ["如何配置 Nginx 多域名和 SSL"],
+        ["llm の事前トレーニングと微調整とは何ですか?またその違いは何ですか"],
+    ],
+    cache_examples=False,
+    title=AI_NAME
+)
+demo.launch()