Create app.py

app.py

@@ -0,0 +1,135 @@
+import os
+import gradio as gr
+
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+
+
+# ───────────────────────── CONFIG ─────────────────────────
+EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+GROQ_MODEL = "llama-3.1-8b-instant"
+TOP_K = 3
+
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+
+
+# ───────────────────────── INIT MODELS ─────────────────────────
+embeddings = HuggingFaceEmbeddings(
+    model_name=EMBED_MODEL,
+    model_kwargs={"device": "cpu"},
+    encode_kwargs={"normalize_embeddings": True}
+)
+
+
+def create_llm():
+    return ChatGroq(
+        model=GROQ_MODEL,
+        temperature=0.2,
+        max_tokens=1024,
+        groq_api_key=os.environ["GROQ_API_KEY"]
+    )
+
+
+RAG_PROMPT = ChatPromptTemplate.from_template("""
+You are a helpful assistant.
+Answer ONLY using the context below.
+If not found, say you don't have enough information.
+Context:
+{context}
+Question: {question}
+Answer:
+""")
+
+
+def format_docs(docs):
+    return "\n\n".join(d.page_content for d in docs)
+
+
+# ───────────────────────── GLOBAL STATE ─────────────────────────
+vectorstore = None
+rag_chain = None
+
+
+# ───────────────────────── PROCESS PDF ─────────────────────────
+def process_pdf(file):
+    global vectorstore, rag_chain
+
+    if file is None:
+        return "Upload a PDF first."
+
+    path = file.name
+
+    # Load
+    loader = PyPDFLoader(path)
+    docs = loader.load()
+
+    # Split
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=500,
+        chunk_overlap=50
+    )
+    chunks = splitter.split_documents(docs)
+
+    # Vector store
+    if vectorstore is None:
+        vectorstore = FAISS.from_documents(chunks, embeddings)
+    else:
+        vectorstore.add_documents(chunks)
+
+    retriever = vectorstore.as_retriever(search_kwargs={"k": TOP_K})
+
+    llm = create_llm()
+
+    rag_chain = (
+        {
+            "context": retriever | format_docs,
+            "question": RunnablePassthrough()
+        }
+        | RAG_PROMPT
+        | llm
+        | StrOutputParser()
+    )
+
+    return f"✅ PDF processed successfully!\nChunks: {len(chunks)}"
+
+
+# ───────────────────────── CHAT FUNCTION ─────────────────────────
+def chat(message, history):
+
+    if rag_chain is None:
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": "Please upload a PDF first."})
+        return "", history
+
+    response = rag_chain.invoke(message)
+
+    history.append({"role": "user", "content": message})
+    history.append({"role": "assistant", "content": response})
+
+    return "", history
+
+
+# ───────────────────────── UI ─────────────────────────
+with gr.Blocks(title="RAG Chatbot") as demo:
+
+    gr.Markdown("## 📄 PDF RAG Chatbot (Groq + FAISS + LangChain)")
+
+    with gr.Row():
+        file = gr.File(label="Upload PDF")
+        upload_btn = gr.Button("Process PDF")
+
+    status = gr.Textbox(label="Status")
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Ask a question")
+
+    upload_btn.click(process_pdf, inputs=file, outputs=status)
+    msg.submit(chat, inputs=[msg, chatbot], outputs=[msg, chatbot])
+
+demo.launch()