Update app.py

app.py

@@ -1,70 +1,41 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+from langchain.llms import HuggingFaceHub
+from langchain.document_loaders import PyPDFLoader
+
+# 1. Load your PDF (upload yourfile.pdf in the Files tab)
+loader = PyPDFLoader("yourfile.pdf")
+documents = loader.load()
+
+# 2. Split into chunks
+text_splitter = CharacterTextSplitter(chunk_size=800, chunk_overlap=100)
+texts = text_splitter.split_documents(documents)
+
+# 3. Create embeddings + vector DB
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+db = FAISS.from_documents(texts, embeddings)
+
+# 4. Build retriever-based chatbot
+retriever = db.as_retriever(search_kwargs={"k": 3})
+
+qa = ConversationalRetrievalChain.from_llm(
+    HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature":0}),
+    retriever=retriever
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+chat_history = []
+
+def respond(message, history):
+    global chat_history
+    result = qa({"question": message, "chat_history": chat_history})
+    chat_history.append((message, result["answer"]))
+    return result["answer"]
 
+# 5. Simple Gradio UI (no login, no upload)
+chatbot = gr.ChatInterface(respond)
 
 if __name__ == "__main__":
-    demo.launch()
+    chatbot.launch()