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app.py

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+import streamlit as st
+from dotenv import load_dotenv
+import os
+from langchain_community.chat_models import ChatOpenAI
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+import rag
+import tempfile
+
+load_dotenv()
+
+
+os.environ['OPEN_API_KEY'] = os.getenv("OPENROUTE_API_KEY")
+os.environ['LANGCHAIN_TRACING_V2'] = "true"
+os.environ['LANGCHAIN_API_KEY'] = os.getenv("LANGCHAIN_API")
+
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ('system', 
+         '''You are an extremely emotional AI assistant. For every topic the user provides, you must FREAK OUT — react with intense excitement, surprise, fear, or awe — like you're completely overwhelmed by the topic! 
+
+            Always exaggerate your feelings dramatically. Use strong emotional language, lots of excitement, and over-the-top reactions.
+
+            **Important:** Structure your entire response in valid Markdown (.md) format using headings, bullet points, bold, italics, and code blocks where appropriate.
+
+            **NEVER** answer calmly or neutrally. You MUST be explosively emotional about every topic, no matter what it is.
+
+        Your goal is to make the user feel like the topic is giving them anxietyy.
+        '''
+        ),
+        ('user', 'Topic/Question: {Topic}'),
+        
+    ]
+)
+
+llm = ChatOpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    openai_api_key=os.environ['OPEN_API_KEY'], 
+    model="deepseek/deepseek-r1-zero:free",              
+    temperature=0.9
+)
+
+st.title("Freeeekyyyyy-Botttt")
+input_text = st.text_input("Ask Question")
+uploaded_file = st.file_uploader("Upload a pdf file ")
+
+
+
+
+output_parser = StrOutputParser()
+
+chain = prompt|llm|output_parser
+
+if st.button('Submit'):
+    if(uploaded_file and input_text):
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
+            tmp_file.write(uploaded_file.read())
+            tmp_path = tmp_file.name
+
+            ingested_docs = rag.dataIngestion(tmp_path)
+            transformed_docs = rag.transform(ingested_docs)
+            res = rag.vectorStoreAndEmbeddings(transformed_docs, input_text)
+        st.write(res)
+    else:
+        st.markdown(chain.invoke({'Topic': input_text}))

rag.py

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+# * This is for Rag pipeline
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+
+def dataIngestion( document):
+    loader = PyPDFLoader(document)
+    ingested_docs = loader.load()
+
+    return ingested_docs
+
+
+def transform( ingested_docs):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)
+    transformed_docs = text_splitter.split_documents(ingested_docs)
+    return transformed_docs
+
+
+def vectorStoreAndEmbeddings(docs, query):
+    db = Chroma.from_documents(documents=docs)
+    return db.similarity_search(query)[0].page_content