app.py

import os
import numpy as np
import faiss
import gradio as gr
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from groq import Groq

# ================================
# 1. Initialize Groq Client
# ================================
# Groq API Key must be set in Hugging Face Space Secrets
# Go to Settings -> Secrets and add Finance_API
GROQ_API_KEY = os.environ.get("Finance_API")
if not GROQ_API_KEY:
    raise ValueError("Please set the Finance_API secret in your Hugging Face Space.")

client = Groq(api_key=GROQ_API_KEY)

# ================================
# 2. Load Datasets (SCRIPT-FREE)
# ================================
print("Loading datasets...")

# Financial News dataset
news_ds = load_dataset("ashraq/financial-news", split="train[:500]")  # reduce size for runtime

# Create a small QA dataset from Financial News (simple heuristic)
qa_docs = []
for item in news_ds:
    headline = item["headline"]
    sentences = headline.split(". ")
    for s in sentences[:2]:
        if len(s) > 30:
            qa_docs.append(f"Question: What does the news say?\nAnswer: {s}")

documents = []

# Add Financial News
for item in news_ds:
    documents.append(item["headline"])

# Add QA docs
documents.extend(qa_docs)

print(f"Total documents loaded: {len(documents)}")

# ================================
# 3. Load Embedding Model
# ================================
print("Loading embedding model...")
embedder = SentenceTransformer("all-MiniLM-L6-v2")

# ================================
# 4. Create Embeddings
# ================================
print("Generating embeddings...")
embeddings = embedder.encode(documents, show_progress_bar=True)
embeddings = np.array(embeddings).astype("float32")

# ================================
# 5. Build FAISS Index
# ================================
print("Building FAISS index...")
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)
print("FAISS index built successfully!")

# ================================
# 6. Retrieve Relevant Documents
# ================================
def retrieve_docs(query, k=5):
    query_embedding = embedder.encode([query])
    query_embedding = np.array(query_embedding).astype("float32")
    distances, indices = index.search(query_embedding, k)
    retrieved = [documents[i] for i in indices[0]]
    return retrieved

# ================================
# 7. Ask Groq LLaMA
# ================================
def ask_llama(context, question):
    prompt = f"""
You are a professional Financial Investment Advisor AI.

You must answer based ONLY on the provided context.
If the context is not enough, say clearly: "I don't have enough data."

Context:
{context}

User Question:
{question}

Answer Format:
1. Investment Suggestion
2. Risk Level (Low/Medium/High)
3. Reasoning (based on context)
4. Recommendation (where to invest more)
"""
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": prompt}],
        model="llama-3.3-70b-versatile"
    )
    return response.choices[0].message.content

# ================================
# 8. Full RAG Pipeline
# ================================
def rag_pipeline(user_question):
    retrieved_docs = retrieve_docs(user_question, k=5)
    context = "\n\n".join(retrieved_docs)
    answer = ask_llama(context, user_question)
    return answer

# ================================
# 9. Gradio UI
# ================================
def chatbot(question):
    return rag_pipeline(question)

interface = gr.Interface(
    fn=chatbot,
    inputs=gr.Textbox(lines=3, placeholder="Example: I have $5000, where should I invest?"),
    outputs="text",
    title="📈 RAG-Based Financial Investment Advisor",
    description="Uses Financial News + FAISS + Groq LLaMA (Script-Free datasets)"
)

interface.launch()