Create app.py

app.py

@@ -0,0 +1,143 @@
+import warnings
+warnings.filterwarnings("ignore")
+
+import uuid
+import gradio as gr
+import os
+
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.chains import RetrievalQA
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+
+# -------------------------
+# LLM (Hugging Face Router API)
+# -------------------------
+
+repo_id = "Qwen/Qwen2.5-7B-Instruct"
+
+llm_endpoint = HuggingFaceEndpoint(
+    repo_id=repo_id,
+    task="text-generation",
+    max_new_tokens=512,
+    temperature=0.1,
+    huggingfacehub_api_token=os.getenv("HF_TOKEN"),
+    provider="hf-inference",   # forces router.huggingface.co
+    model_kwargs={"wait_for_model": True},
+)
+
+llm = ChatHuggingFace(llm=llm_endpoint)
+
+
+# -------------------------
+# Document loader
+# -------------------------
+
+def document_loader(file_path: str):
+    loader = PyPDFLoader(file_path)
+    return loader.load()
+
+
+# -------------------------
+# Text splitter
+# -------------------------
+
+def text_splitter(data):
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=50,
+        length_function=len,
+    )
+    return splitter.split_documents(data)
+
+
+# -------------------------
+# Embedding model
+# -------------------------
+
+def embedding_model():
+    return HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2"
+    )
+
+
+# -------------------------
+# Vector DB
+# -------------------------
+
+def vector_database(chunks):
+    chunks = [
+        c for c in chunks
+        if c.page_content and c.page_content.strip() and len(c.page_content.strip()) > 10
+    ]
+
+    vectordb = Chroma.from_documents(
+        documents=chunks,
+        embedding=embedding_model(),
+        collection_name=f"pdf_chat_{uuid.uuid4().hex}",
+    )
+    return vectordb
+
+
+# -------------------------
+# Retriever
+# -------------------------
+
+def build_retriever(file_path: str):
+    docs = document_loader(file_path)
+    chunks = text_splitter(docs)
+    vectordb = vector_database(chunks)
+    return vectordb.as_retriever()
+
+
+# -------------------------
+# QA Chain
+# -------------------------
+
+def retriever_qa(file, query):
+    retr = build_retriever(file)
+
+    qa = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=retr,
+        return_source_documents=False,
+    )
+
+    result = qa.invoke({"query": query})
+    return result["result"]
+
+
+# -------------------------
+# Gradio interface
+# -------------------------
+
+rag_application = gr.Interface(
+    fn=retriever_qa,
+    allow_flagging="never",
+    inputs=[
+        gr.File(
+            label="Upload PDF File",
+            file_count="single",
+            file_types=[".pdf"],
+            type="filepath",
+        ),
+        gr.Textbox(
+            label="Input Query",
+            lines=2,
+            placeholder="Type your question here...",
+        ),
+    ],
+    outputs=gr.Textbox(label="Output"),
+    title="PDF QA Chatbot",
+    description=(
+        "Upload a PDF document and ask any question. "
+        "The chatbot will try to answer using the provided document."
+    ),
+)
+
+if __name__ == "__main__":
+    rag_application.launch()