Upload app.py

app.py

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+import os
+import zipfile
+import gradio as gr
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+
+# --- Step 1: Unzip FAISS index ---
+if not os.path.exists("faiss_index") and os.path.exists("faiss_index.zip"):
+    with zipfile.ZipFile("faiss_index.zip", "r") as zip_ref:
+        zip_ref.extractall(".")
+
+# --- Step 2: Load embedding and vectorstore ---
+embedding_model = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+vectordb = FAISS.load_local("faiss_index", embedding_model)
+
+# --- Step 3: Load the LLM ---
+model_id = "tiiuae/falcon3-1b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    pad_token_id=tokenizer.eos_token_id,
+    max_new_tokens=200,
+    do_sample=True,
+    temperature=1.0,
+)
+llm = HuggingFacePipeline(pipeline=pipe)
+
+# --- Step 4: Setup memory and QA chain ---
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+prompt = PromptTemplate.from_template("""
+You are a helpful assistant at the University of Hertfordshire. Use the context below to answer the question clearly and factually.
+If the answer is not in the context, say you don't know.
+
+Context:
+{context}
+
+Question:
+{question}
+
+Helpful Answer:
+""")
+
+qa_chain = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    retriever=vectordb.as_retriever(search_kwargs={"k": 3}),
+    memory=memory,
+    chain_type="map_reduce",
+    combine_docs_chain_kwargs={"prompt": prompt}
+)
+
+# --- Step 5: Define chatbot logic ---
+def chat(message, history):
+    result = qa_chain.invoke({"question": message})
+    response = result.get("answer", "")
+    response = response.split("Answer:")[-1].replace("<|assistant|>", "").strip()
+    return response
+
+# UH logo
+UH_LOGO = "images/UH.png"
+
+# --- Step 6: UI ---
+sample_questions = [
+    "How do I register as a new student?",
+    "Where can I find accommodation?",
+    "Can I renew my tenancy agreement?",
+    "What do I do on my first day?",
+]
+
+with gr.Blocks() as demo:
+    gr.Image(UH_LOGO, show_label=False, container=False, scale=1)
+    gr.Markdown("## ASK Herts Students Help Chatbot 🤖")
+
+    chatbot = gr.Chatbot()
+    txt = gr.Textbox(placeholder="Ask me anything about university life...", label="Your question")
+    submit = gr.Button("Submit")
+
+    gr.Markdown("#### 💡 Sample Questions:")
+    with gr.Row():
+        for q in sample_questions:
+            gr.Button(q).click(lambda x=q: gr.update(value=x), outputs=[txt])
+
+    def respond(message, history):
+        answer = chat(message, history)
+        history.append((message, answer))
+        return "", history
+
+    submit.click(respond, [txt, chatbot], [txt, chatbot])
+    txt.submit(respond, [txt, chatbot], [txt, chatbot])
+
+demo.launch()