updating req file

api.py

@@ -11,9 +11,10 @@ from bs4 import BeautifulSoup
 import requests
 import torch
 from dotenv import load_dotenv
-
+import os
 from fastapi.middleware.cors import CORSMiddleware
 
+
 MODEL_PATH = "lib/20_lstm_model.h5"
 model = tf.keras.models.load_model(MODEL_PATH)
 
@@ -52,7 +53,6 @@ def fetch_and_process_ticker_data(ticker, start_date, end_date, interval="1d"):
         temp["revenue"] = temp["adjclose"] * temp["volume"]
         temp["daily_profit"] = temp["adjclose"] - temp["open"]
         df = pd.concat([df, temp], axis=0)
-        df.to_csv("api_test.csv", index=False)
 
     except Exception as error:
         raise HTTPException(
@@ -291,17 +291,17 @@ async def fetch_ticker_data(ticker_name: str, start_date: str, end_date: str, in
         raise HTTPException(status_code=500, detail=str(e))
 
 
-@app.post("/predict-prices/")
-async def predict_prices(request: TickerRequest):
+@app.get("/predict-prices/{ticker_name}/{start_date}/{end_date}/{interval}")
+async def predict_prices(ticker_name: str, start_date: str, end_date: str, interval: str):
     try:
-        raw_data = fetch_and_process_ticker_data(
-            ticker=request.ticker,
-            start_date=request.start_date,
-            end_date=request.end_date,
-            interval=request.interval
+        result_df = fetch_and_process_ticker_data(
+            ticker=ticker_name,
+            start_date=start_date,
+            end_date=end_date,
+            interval=interval
         )
 
-        raw_data = raw_data.tail(60)
+        raw_data = result_df.tail(60)
         raw_data = raw_data.reset_index()
 
         raw_data.rename(columns={"index": "date"}, inplace=True)
@@ -319,15 +319,13 @@ async def predict_prices(request: TickerRequest):
 
         lstm_pred_df = storing_predictions(
             temp_df, dates, stock, combined_dataset_prediction_inverse)
-        news_df = scrape_news(ticker_name=request.ticker)
+        news_df = scrape_news(ticker_name=ticker_name)
 
         combined_with_news_df = add_recent_news(lstm_pred_df, news_df)
         sentiment_df = news_sentiment(combined_with_news_df)
 
         sentiment_df['time_idx'] = range(1, len(sentiment_df) + 1)
 
-        print(sentiment_df)
-
         predicted_values = get_tft_predictions(sentiment_df)
 
         final_pred_open_price = predicted_values[0].item()
@@ -339,9 +337,4 @@ async def predict_prices(request: TickerRequest):
         raise HTTPException(status_code=500, detail=str(e))
 
 
-# @app.get("/query-rag/{user_query}")
-# def query_rag(user_query: str):
-
-#     response = query_engine.query(user_query)
 
-#     return {'message': response}

rag_index/default__vector_store.json

@@ -1,3 +0,0 @@
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-size 14374003

rag_index/docstore.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bbf3547cf968b289a8fff77a16cb0143649df8424f45843822ad9c6853bf3d45
-size 7500231

rag_index/graph_store.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8e0a77744010862225c69da83c585f4f8a42fd551b044ce530dbb1eb6e16742c
-size 18

rag_index/image__vector_store.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d17ed74c1649a438e518a8dc56a7772913dfe1ea7a7605bce069c63872431455
-size 72

rag_index/index_store.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0128597297ccb9b86477e4805882f2501c988031e30cc651d857ad3a5a3b870c
-size 133807