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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+train_data.csv filter=lfs diff=lfs merge=lfs -text

requirements.txt

@@ -1,3 +1,8 @@
-altair
+streamlit
+faiss-cpu
+numpy
+sentence-transformers
+transformers
+torch
 pandas
-streamlit
+huggingface_hub

smartbot.py

@@ -0,0 +1,131 @@
+import streamlit as st
+import faiss
+import numpy as np
+import pandas as pd
+import torch
+import os
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import login
+
+# --- HuggingFace login ---
+HF_TOKEN = os.getenv("HF_TOKEN")
+if HF_TOKEN:
+    login(token=HF_TOKEN)
+
+# --- Load data ---
+train = pd.read_csv('train_data.csv')  # Assumes it's in the same directory
+questions = train['question'].tolist()
+answers = train['answer'].tolist()
+
+qa_pairs = [f"Q: {q} A: {a}" for q, a in zip(questions, answers)]
+
+# --- Embedding model ---
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+answer_embeddings = embedding_model.encode(answers)
+
+# --- FAISS index ---
+index = faiss.IndexFlatL2(answer_embeddings.shape[1])
+index.add(np.array(answer_embeddings))
+
+# --- LLaMA model setup ---
+model_name = "meta-llama/Llama-3-8B-Instruct"  # Update to a valid space-available model if needed
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+# --- Helper Functions ---
+def sanitize_answer(question, answer):
+    return any(word.lower() in answer.lower() for word in question.lower().split())
+
+recent_questions = {}
+
+def is_finance_question(user_query):
+    check_prompt = (
+        f"You are a financial expert. Determine whether the following question is clearly about finance:\n\n"
+        f"Question: {user_query}\n\n"
+        f"Respond only with 'Yes' or 'No'."
+    )
+    input_ids = tokenizer(check_prompt, return_tensors="pt").to(model.device)
+    output_ids = model.generate(
+        **input_ids,
+        max_new_tokens=10,
+        temperature=0.0,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
+    return response.lower().startswith("yes")
+
+def ask_finance_bot(user_query, top_k=3):
+    normalized_query = user_query.lower().strip()
+    count = recent_questions.get(normalized_query, 0) + 1
+    recent_questions[normalized_query] = count
+
+    # Embed user query
+    query_embedding = embedding_model.encode([user_query])
+    D, I = index.search(np.array(query_embedding), top_k)
+    retrieved_answers = [answers[i] for i in I[0]]
+    context = "\n".join([f"- {text}" for text in retrieved_answers])
+
+    temperature = min(0.7 + 0.1 * (count - 1), 1.0)
+
+    instruction = (
+        "You are a highly knowledgeable AI assistant specializing strictly in finance.\n"
+        "Strictly answer only financially related topics.\n"
+        "Never answer questions that are not financially related.\n"
+        "Do not answer anything outside finance.\n"
+        "Always provide accurate, objective, and concise answers to financial questions.\n"
+        "Avoid unnecessary elaboration and focus directly on answering the user's query.\n"
+        "Use the background context only if it is accurate, clear, and relevant. If the context is unclear, incomplete, low-quality, or irrelevant, ignore it and generate your own correct, concise financial answer.\n"
+        "Do not copy or repeat the context verbatim — instead, synthesize your own response based on it.\n"
+        "Do not speculate or use personal phrases like 'I think' or 'In my opinion'.\n"
+        "If a valid financial question is asked, always answer — never refuse or say 'I can't help with that.'\n"
+        "If a question is unrelated to finance, respond: 'I'm specialized in finance and can't help with that. How can I assist you with a finance-related question today?'\n"
+        "If a greeting like 'Hi', 'Hello', or 'Hey' is used, respond with: 'Hello! How can I help you with your finance-related question today?'\n"
+    )
+
+    for _ in range(6):
+        prompt = f"""{instruction}
+
+Background context:
+{context}
+
+User question: {user_query}
+
+Answer:"""
+
+        input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
+        output_ids = model.generate(
+            **input_ids,
+            max_new_tokens=256,
+            temperature=temperature,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+        response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        answer_text = response.split("Answer:")[-1].strip()
+
+        if sanitize_answer(user_query, answer_text):
+            return answer_text
+
+    return "I'm not confident in the response. Please consult a certified financial expert."
+
+# --- Streamlit App UI ---
+st.set_page_config(page_title="DiMowkayBot - Finance Assistant", layout="centered")
+st.title("💸 DiMowkayBot - Your Finance Q&A Assistant")
+
+user_query = st.text_input("Enter your finance-related question:")
+
+if user_query:
+    if not is_finance_question(user_query):
+        st.warning("I'm specialized in finance and can't help with that. How can I assist you with a finance-related question today?")
+    else:
+        with st.spinner("Thinking..."):
+            answer = ask_finance_bot(user_query)
+        st.success("Response:")
+        st.write(answer)

train_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b625b903a78f1367441c595d1285522b74cb005d43c0f44d08ee7be2e6119e0
+size 13054424