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app.py

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+# app.py
+
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader
+from langchain.chains import RetrievalQA
+from langchain.llms.base import LLM
+
+from typing import List, Optional
+from groq import Groq
+import gradio as gr
+import os
+import uuid
+
+# ✅ Groq LLM Wrapper
+class GroqLLM(LLM):
+    model: str = "llama3-8b-8192"
+    api_key: str = os.environ.get("GROQ_API_KEY")  # Use env variable for safety
+    temperature: float = 0.0
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        client = Groq(api_key=self.api_key)
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ]
+        response = client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            temperature=self.temperature,
+        )
+        return response.choices[0].message.content
+
+    @property
+    def _llm_type(self) -> str:
+        return "groq-llm"
+
+# ✅ Session Store
+session_store = {}
+
+# ✅ Process PDF File
+def process_pdf_and_setup_chain(pdf_file):
+    if not pdf_file:
+        return "❌ No PDF uploaded."
+
+    file_path = pdf_file.name
+    temp_dir = f"temp_{uuid.uuid4().hex}"
+    os.makedirs(temp_dir, exist_ok=True)
+
+    try:
+        loader = PyPDFLoader(file_path)
+        documents = loader.load()
+
+        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+        docs = splitter.split_documents(documents)
+
+        embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        vectorstore = Chroma.from_documents(docs, embedding, persist_directory=os.path.join(temp_dir, "chroma"))
+
+        retriever = vectorstore.as_retriever()
+        groq_llm = GroqLLM()
+
+        qa_chain = RetrievalQA.from_chain_type(
+            llm=groq_llm,
+            retriever=retriever,
+            return_source_documents=True
+        )
+
+        session_store["qa_chain"] = qa_chain
+        session_store["temp_dir"] = temp_dir
+
+        return "✅ PDF processed! You can now ask questions."
+
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# ✅ Answering Function
+def answer_question(query):
+    qa_chain = session_store.get("qa_chain")
+    if not qa_chain:
+        return "❌ Please upload and process a PDF first."
+    if not query.strip():
+        return "❗ Please enter a question."
+    try:
+        result = qa_chain({"query": query})
+        return result["result"]
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# ✅ Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 📄 PDF Q&A with LangChain + Groq LLaMA3")
+    gr.Markdown("Upload a PDF, process it, and ask any question from its content.")
+
+    with gr.Row():
+        pdf_input = gr.File(label="📄 Upload PDF", file_types=[".pdf"])
+        process_btn = gr.Button("⚙️ Process PDF")
+
+    status = gr.Textbox(label="Status", interactive=False)
+
+    with gr.Row():
+        question = gr.Textbox(label="Ask a question", lines=2, placeholder="e.g. What is the document about?")
+        ask_btn = gr.Button("🔍 Ask")
+
+    answer = gr.Textbox(label="Answer", interactive=False)
+
+    process_btn.click(fn=process_pdf_and_setup_chain, inputs=pdf_input, outputs=status)
+    ask_btn.click(fn=answer_question, inputs=question, outputs=answer)
+
+demo.launch()
+

requirements.txt

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+langchain
+langchain-community
+openai
+chromadb
+faiss-cpu
+sentence-transformers
+tiktoken
+groq
+pypdf
+gradio