Create app.py

app.py

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+import os
+import pandas as pd
+from datasets import load_dataset
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+import gradio as gr
+
+# Load environment variables from .env file
+load_dotenv()
+groq_key = os.environ.get('groq_api_keys')
+# Initialize LLM
+llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=groq_key)
+
+print("✅ Setup complete. API Key loaded.")
+
+
+# Load data from huggingface for astro arxiv papers
+ds = load_dataset("mehnaazasad/arxiv_astro_co_ga")
+data = ds["test"]["abstract"][:50] # take first examples
+
+
+# 1. Initialize the Embedding Model (Converts text to math)
+embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+
+# 2. Create and Populate Vector Store
+vectorstore = Chroma(
+    collection_name="dataset_store",
+    embedding_function=embed_model,
+    persist_directory="./chroma_db",
+)
+
+
+vectorstore.add_texts(data)
+retriever = vectorstore.as_retriever()
+
+
+
+print("🧠 Vector Store created. The AI can now 'search' your data.")
+
+
+template = """You are astronomy expert. 
+Use the provided context to answer the question. 
+If you don't know, say you don't know. Explain in detail.
+Context: {context}
+Question: {question}
+Answer:"""
+
+rag_prompt = PromptTemplate.from_template(template)
+
+rag_chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | rag_prompt
+    | llm
+    | StrOutputParser()
+)
+print("⛓️ RAG Chain is ready.")
+
+def rag_memory_stream(text):
+    partial_text = ""
+    for new_text in rag_chain.stream(text):
+        partial_text += new_text
+        yield partial_text
+
+demo = gr.Interface(
+    title="Real-time Astronomy AI Assistant",
+    fn=rag_memory_stream,
+    inputs="text",
+    outputs="text",
+    examples=['what are the characteristics of blue compact dwarf?', 'What is cold dark matter?'],
+)
+
+
+demo.launch()
+