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chatbot.py

@@ -0,0 +1,67 @@
+import os
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain_groq import ChatGroq
+import gradio as gr
+
+# Load Groq API key from env variables
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+def load_and_index_pdf(pdf_path="company_data.pdf"):
+    loader = PyPDFLoader(pdf_path)
+    documents = loader.load()
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    texts = splitter.split_documents(documents)
+    embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    db = FAISS.from_documents(texts, embedding)
+    db.save_local("company_faiss_index")
+
+def setup_qa():
+    embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    if not os.path.exists("company_faiss_index"):
+        load_and_index_pdf()
+    db = FAISS.load_local("company_faiss_index", embedding, allow_dangerous_deserialization=True)
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+    llm = ChatGroq(model_name="llama3-70b-8192", api_key=groq_api_key)
+
+    prompt = PromptTemplate.from_template("""
+You are a helpful assistant for a digital marketing company.
+Try to answer the user's question based on the provided context from the company document.
+If the answer is not found in the context, provide a helpful and accurate answer from your own knowledge, focusing on digital marketing topics.
+
+Context:
+{context}
+
+Question:
+{question}
+""")
+
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        retriever=retriever,
+        return_source_documents=False,
+        chain_type_kwargs={"prompt": prompt}
+    )
+    return qa_chain
+
+qa_chain = setup_qa()
+
+def answer_question(query):
+    result = qa_chain.invoke(query)
+    return result['result']
+
+# Minimal Gradio UI
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=gr.Textbox(lines=2, placeholder="Ask a question about digital marketing..."),
+    outputs="text",
+    title="CLick Media Lab Chatbot"
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -1 +1,7 @@
-huggingface_hub==0.25.2
+langchain==0.0.250
+langchain-huggingface
+langchain-community
+langchain-groq
+gradio
+faiss-cpu
+python-dotenv