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app.py

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+import gradio as gr
+from langchain import HuggingFacePipeline
+from transformers import pipeline
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+from langchain.embeddings import HuggingFaceEmbeddings
+
+# --- Load free Hugging Face LLM ---
+# You can switch to another free model if you like
+hf_pipeline = pipeline(
+    "text-generation",
+    model="tiiuae/falcon-7b-instruct",
+    max_new_tokens=256,
+    temperature=0.5
+)
+llm = HuggingFacePipeline(pipeline=hf_pipeline)
+
+# --- PDF Loader ---
+def document_loader(file):
+    loader = PyPDFLoader(file.name)
+    return loader.load()
+
+# --- Text Splitter ---
+def text_splitter(documents):
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=20
+    )
+    return splitter.split_documents(documents)
+
+# --- Vector Database using embeddings ---
+def vector_database(chunks):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vectordb = Chroma.from_documents(chunks, embeddings)
+    return vectordb
+
+# --- Build retriever ---
+def retriever(file):
+    chunks = text_splitter(document_loader(file))
+    vectordb = vector_database(chunks)
+    return vectordb.as_retriever()
+
+# --- RetrievalQA ---
+def retriever_qa(file, query):
+    retriever_obj = retriever(file)
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=retriever_obj,
+        return_source_documents=False
+    )
+    return qa_chain.run(query)
+
+# --- Gradio Interface ---
+app = gr.Interface(
+    fn=retriever_qa,
+    inputs=[
+        gr.File(label="Upload PDF", file_types=['.pdf']),
+        gr.Textbox(label="Ask a question about the PDF", lines=2, placeholder="Type your question here...")
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="Free LLM PDF Q&A Bot",
+    description="Upload a PDF document and ask any question. This bot uses a free open-source LLM and vector search to answer your questions."
+)
+
+if __name__ == "__main__":
+    app.launch()
+

requirements.txt

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+transformers
+torch
+langchain
+chromadb
+sentence-transformers
+gradio