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app.py

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+
+# app.py
+import os
+import gradio as gr
+
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.document_loaders import TextLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.chains import RetrievalQA
+from langchain.llms import HuggingFacePipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# Optional: Set HF Token if needed
+# os.environ['HUGGINGFACEHUB_API_TOKEN'] = 'hf_XXXX'
+
+# Initialize embedding model
+embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+# Load HF model (lightweight for CPU)
+model_name = "google/flan-t5-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Wrap in pipeline
+pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, max_length=512)
+llm = HuggingFacePipeline(pipeline=pipe)
+
+def process_file(file_path):
+    # Load & split document
+    loader = TextLoader(file_path)
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    docs = text_splitter.split_documents(documents)
+
+    # Create vector DB
+    vector_db = FAISS.from_documents(docs, embedding_model)
+    retriever = vector_db.as_retriever()
+
+    # Setup RetrievalQA chain
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=retriever
+    )
+
+    return qa_chain
+
+# Store the QA chain globally (across UI events)
+qa_chain = None
+
+def upload_and_prepare(file):
+    global qa_chain
+    # qa_chain = process_file(file)
+    qa_chain = process_file(file.name)
+    return "✅ Document processed. You can now ask questions!"
+
+def ask_question(query):
+    if not qa_chain:
+        return "❌ Please upload a document first."
+    response = qa_chain.invoke({"query": query})
+    return response["result"]
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 Ask Questions About Your Document (LangChain + Hugging Face)")
+    
+    with gr.Row():
+        file_input = gr.File(label="📄 Upload .txt File", type="filepath")
+        upload_btn = gr.Button("🔄 Process Document")
+
+    upload_output = gr.Textbox(label="📁 Status", interactive=False)
+
+    with gr.Row():
+        query_input = gr.Textbox(label="❓ Your Question")
+        query_btn = gr.Button("🧠 Get Answer")
+
+    answer_output = gr.Textbox(label="✅ Answer", lines=4)
+
+    upload_btn.click(upload_and_prepare, inputs=file_input, outputs=upload_output)
+    query_btn.click(ask_question, inputs=query_input, outputs=answer_output)
+
+# For local dev use: demo.launch()
+# For HF Spaces
+if __name__ == "__main__":
+    demo.launch()
+
+

requirements.txt

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+
+gradio==5.36.2
+
+transformers==4.53.3
+
+sentence-transformers==3.0.1
+
+langchain==0.3.27
+
+faiss-cpu==1.8.0
+
+langchain-community==0.3.27
+
+numpy<2