Update app.py

app.py

@@ -1,15 +1,21 @@
+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
-from google.colab import userdata
 
 # ================================
 # 1. Initialize Groq Client
 # ================================
-client = Groq(api_key=userdata.get("Finance_API"))
+# 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)
@@ -17,36 +23,26 @@ client = Groq(api_key=userdata.get("Finance_API"))
 print("Loading datasets...")
 
 # Financial News dataset
-news_ds = load_dataset("ashraq/financial-news", split="train[:1000]")
-
-# NOTE: Removing problematic stock market dataset loading for now.
-# stock_ds = load_dataset("ashraq/stock_price_data", split="train[:1000]")
+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:
-    # Changed 'text' to 'headline'
     headline = item["headline"]
-    # Split into sentences and make simple Q/A
     sentences = headline.split(". ")
-    for s in sentences[:2]:  # take first 2 sentences to reduce size
-        if len(s) > 30:  # ignore very short sentences
+    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:
-    # Changed 'text' to 'headline'
     documents.append(item["headline"])
 
-# Add simple QA docs
+# Add QA docs
 documents.extend(qa_docs)
 
-# NOTE: Removed stock dataset from document aggregation as well.
-# for item in stock_ds:
-#     documents.append(str(item))
-
 print(f"Total documents loaded: {len(documents)}")
 
 # ================================
@@ -129,7 +125,7 @@ interface = gr.Interface(
     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 + Stock Index datasets + FAISS + Groq LLaMA (No scripts needed)"
+    description="Uses Financial News + FAISS + Groq LLaMA (Script-Free datasets)"
 )
 
-interface.launch(share=True)
+interface.launch()