Update app.py

app.py

@@ -1,138 +1,138 @@
-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))
+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:]
+    ])
+    
+    context_text = "\n\n---\n\n".join(context)
+
+    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:
+{context_text}
+
+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))
     )