Upload 3 files

app.py

@@ -1,70 +1,138 @@
-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)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
-if __name__ == "__main__":
-    demo.launch()
+import os
+import json
+import datetime
+import threading
+from pathlib import Path
+
+import numpy as np
+import gradio as gr
+from dotenv import load_dotenv
+from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings
+
+# ================= ENV =================
+load_dotenv()
+NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY")
+if not NVIDIA_API_KEY:
+    raise RuntimeError("NVIDIA_API_KEY not found")
+
+os.environ["NVIDIA_API_KEY"] = NVIDIA_API_KEY
+
+# ================= CONFIG =================
+DAILY_LIMIT = 50
+RATE_FILE = Path("rate_limit.json")
+EMBEDDINGS_FILE = Path("embeddings.json")
+MAX_HISTORY_TURNS = 3   # keep last 3 Q/A pairs
+
+lock = threading.Lock()
+
+# ================= MODELS =================
+embedder = NVIDIAEmbeddings(
+    model="nvidia/nv-embed-v1",
+    truncate="END"
+)
+
+llm = ChatNVIDIA(
+    model="mistralai/mixtral-8x22b-instruct-v0.1",
+    temperature=0.2
+)
+
+# ================= LOAD DOCS =================
+with open(EMBEDDINGS_FILE) as f:
+    DOCS = json.load(f)
+
+# ================= UTILS =================
+def cosine(a, b):
+    a, b = np.array(a), np.array(b)
+    return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
+
+def retrieve(question, k=4):
+    qvec = embedder.embed_query(question)
+    scored = [(cosine(qvec, d["embedding"]), d["text"]) for d in DOCS]
+    scored.sort(reverse=True)
+    return [t for _, t in scored[:k]]
+
+def check_rate_limit():
+    today = datetime.date.today().isoformat()
+    with lock:
+        data = json.loads(RATE_FILE.read_text()) if RATE_FILE.exists() else {}
+        if data.get(today, 0) >= DAILY_LIMIT:
+            return False
+        data[today] = data.get(today, 0) + 1
+        RATE_FILE.write_text(json.dumps(data))
+    return True
+
+def build_prompt(context, history, question):
+    history_text = "\n".join(
+        f"User: {q}\nAssistant: {a}"
+        for q, a in history[-MAX_HISTORY_TURNS:]
+    )
+
+    return f"""
+You are a document-grounded assistant.
+Answer ONLY using the context.
+If the answer is not present, say "I don't know".
+
+Conversation so far:
+{history_text}
+
+Context:
+{"\n\n---\n\n".join(context)}
+
+User question:
+{question}
+""".strip()
+
+# ================= CHAT FN (STREAMING) =================
+def chat_stream(question, history):
+    if not question.strip():
+        yield history
+        return
+
+    if not check_rate_limit():
+        history.append((question, "Daily limit reached (50 queries)."))
+        yield history
+        return
+
+    context = retrieve(question)
+    prompt = build_prompt(context, history, question)
+
+    partial = ""
+    for chunk in llm.stream(prompt):
+        partial += chunk.content
+        yield history + [(question, partial)]
+
+# ================= UI =================
+with gr.Blocks(title="Academic Regulations RAG") as demo:
+    gr.Markdown("## 📘 Academic Regulations Queries")
+    gr.Markdown(
+        "Ask questions about the academic regulations document. "
+        "Answers are generated **only** from the official document."
+    )
+
+    chatbot = gr.Chatbot(height=420)
+    
+    question = gr.Textbox(
+        placeholder="e.g. What is the E grade?",
+        label="Your question",
+        scale=4
+    )
+    ask = gr.Button("Ask", scale=1, min_width=100)
+    
+    clear = gr.Button("Clear Chat")
+
+    ask.click(chat_stream, [question, chatbot], chatbot)
+
+    question.submit(
+        chat_stream,
+        inputs=[question, chatbot],
+        outputs=chatbot
+    )
+
+    clear.click(lambda: [], None, chatbot)
+
+# ================= RUN =================
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=int(os.getenv("PORT", 7860))
+    )

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+numpy
+python-dotenv
+langchain-nvidia-ai-endpoints