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  1. AAPL_data.csv +757 -0
  2. AMZN_data.csv +757 -0
  3. MSFT_data.csv +746 -0
  4. ModelServe.py +69 -0
  5. TSLA_data.csv +744 -0
  6. app.py +73 -0
AAPL_data.csv ADDED
@@ -0,0 +1,757 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ Date,Open,High,Low,Close,Adj Close,Volume
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1
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2
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757
+ 2022-12-30,83.12000274658203,84.05000305175781,82.47000122070312,84.0,84.0,62401200
MSFT_data.csv ADDED
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1
+ Date,Open,High,Low,Close,Adj Close,Volume
2
+ 2020-01-07,159.32000732421875,159.6699981689453,157.32000732421875,157.5800018310547,152.8137664794922,21634100
3
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+ 2020-01-13,161.75999450683594,163.30999755859375,161.25999450683594,163.27999877929688,158.3413543701172,21626500
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+ 2020-01-14,163.38999938964844,163.60000610351562,161.72000122070312,162.1300048828125,157.2261505126953,23477400
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+ 2020-01-15,162.6199951171875,163.94000244140625,162.57000732421875,163.17999267578125,158.24435424804688,21417900
9
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+ 2020-01-17,167.4199981689453,167.47000122070312,165.42999267578125,167.10000610351562,162.0458221435547,34371700
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30
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35
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46
+ 2020-03-11,157.1300048828125,157.6999969482422,151.14999389648438,153.6300048828125,149.39016723632812,56371600
47
+ 2020-03-12,145.3000030517578,153.47000122070312,138.5800018310547,139.05999755859375,135.2222900390625,93226400
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+ 2020-03-13,147.5,161.91000366210938,140.72999572753906,158.8300018310547,154.4466552734375,92727400
49
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51
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52
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56
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+ 2020-03-27,151.75,154.88999938964844,149.1999969482422,149.6999969482422,145.56861877441406,57042300
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+ 2020-03-30,152.44000244140625,160.60000610351562,150.00999450683594,160.22999572753906,155.8080291748047,63420300
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+ 2020-03-31,159.39999389648438,164.77999877929688,156.55999755859375,157.7100067138672,153.35757446289062,77927200
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+ 2020-04-01,153.0,157.75,150.82000732421875,152.11000061035156,147.912109375,57969900
62
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+ 2020-04-06,160.32000732421875,166.5,157.5800018310547,165.27000427246094,160.7089385986328,67111700
65
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66
+ 2020-04-08,165.6699981689453,166.6699981689453,163.5,165.1300048828125,160.57276916503906,48318200
67
+ 2020-04-09,166.36000061035156,167.3699951171875,163.3300018310547,165.13999938964844,160.58251953125,51385100
68
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70
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690
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691
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ModelServe.py ADDED
@@ -0,0 +1,69 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ import numpy as np
3
+ from fastapi import FastAPI, HTTPException
4
+ from pydantic import BaseModel
5
+ from tensorflow.keras.models import Sequential
6
+ from tensorflow.keras.layers import LSTM, Dense, Dropout
7
+ from sklearn.preprocessing import MinMaxScaler
8
+
9
+ app = FastAPI()
10
+
11
+ class StockRequest(BaseModel):
12
+ stock_name: str
13
+
14
+ # Define file paths (assuming the structure is already known)
15
+ STOCK_FILE_PATHS = {
16
+ "TSLA": "E:/Data Science/Stock Price Prediction/Stock Price Prediction/data/TSLA_data.csv",
17
+ "AAPL": "E:/Data Science/Stock Price Prediction/Stock Price Prediction/data/AAPL_data.csv",
18
+ "AMZN": "E:/Data Science/Stock Price Prediction/Stock Price Prediction/data/AMZN_data.csv",
19
+ "MSFT": "E:/Data Science/Stock Price Prediction/Stock Price Prediction/data/MSFT_data.csv",
20
+ }
21
+
22
+ def load_and_preprocess_data(filepath):
23
+ df = pd.read_csv(filepath)
24
+ df['Close'] = df['Close'].astype(float) # Ensure data is float
25
+ scaler = MinMaxScaler(feature_range=(0, 1))
26
+ scaled_data = scaler.fit_transform(df['Close'].values.reshape(-1, 1))
27
+ return scaled_data, scaler
28
+
29
+ def prepare_datasets(data, seq_len):
30
+ x, y = [], []
31
+ for i in range(seq_len, len(data)):
32
+ x.append(data[i-seq_len:i, 0])
33
+ y.append(data[i, 0])
34
+ x, y = np.array(x), np.array(y)
35
+ x = np.reshape(x, (x.shape[0], x.shape[1], 1))
36
+ return x, y
37
+
38
+ @app.post("/LSTM_Predict")
39
+ async def predict(stock_request: StockRequest):
40
+ stock_name = stock_request.stock_name
41
+ if stock_name not in STOCK_FILE_PATHS:
42
+ raise HTTPException(status_code=422, detail="Invalid Stock Name")
43
+
44
+ filepath = STOCK_FILE_PATHS[stock_name]
45
+ data, scaler = load_and_preprocess_data(filepath)
46
+ seq_len = 60 # Sequence length for LSTM
47
+ x_train, y_train = prepare_datasets(data[:int(0.8 * len(data))], seq_len)
48
+ x_test = prepare_datasets(data[int(0.8 * len(data)):], seq_len)[0] # Only x_test needed
49
+
50
+ # Define and compile the model
51
+ model = Sequential([
52
+ LSTM(128, return_sequences=True, input_shape=(seq_len, 1)),
53
+ Dropout(0.2),
54
+ LSTM(64, return_sequences=False),
55
+ Dropout(0.2),
56
+ Dense(1)
57
+ ])
58
+ model.compile(optimizer='adam', loss='mean_squared_error')
59
+
60
+ # Fit model
61
+ model.fit(x_train, y_train, epochs=5, batch_size=32)
62
+
63
+ # Predicting
64
+ predictions = model.predict(x_test)
65
+ predictions = scaler.inverse_transform(predictions) # Inverse transform to get actual values
66
+
67
+ predict_prices = predictions.flatten().tolist() # Convert to list for JSON response
68
+ return {"prediction": predict_prices}
69
+
TSLA_data.csv ADDED
@@ -0,0 +1,744 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Date,Open,High,Low,Close,Adj Close,Volume
2
+ 2020-01-09,33.13999938964844,33.253334045410156,31.524667739868164,32.089332580566406,32.089332580566406,426606000
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5
+ 2020-01-14,36.284000396728516,36.49399948120117,34.99333190917969,35.861331939697266,35.861331939697266,434943000
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+ 2020-01-15,35.31733322143555,35.85599899291992,34.452667236328125,34.56666564941406,34.56666564941406,260532000
7
+ 2020-01-16,32.91666793823242,34.297332763671875,32.81133270263672,34.232666015625,34.232666015625,326050500
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+ 2020-01-17,33.840667724609375,34.37799835205078,33.54399871826172,34.03333282470703,34.03333282470703,204436500
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+ 2020-01-23,37.616668701171875,38.79999923706055,37.040000915527344,38.14666748046875,38.14666748046875,294765000
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+ 2020-01-24,38.04199981689453,38.25733184814453,36.95066833496094,37.654666900634766,37.654666900634766,215304000
13
+ 2020-01-27,36.132667541503906,37.62933349609375,35.95199966430664,37.201332092285156,37.201332092285156,204121500
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+ 2020-01-31,42.66666793823242,43.53333282470703,42.167999267578125,43.371334075927734,43.371334075927734,235789500
18
+ 2020-02-03,44.91266632080078,52.409332275390625,44.90133285522461,52.0,52.0,705975000
19
+ 2020-02-04,58.86399841308594,64.59933471679688,55.59199905395508,59.137332916259766,59.137332916259766,914082000
20
+ 2020-02-05,54.88399887084961,56.39866638183594,46.94066619873047,48.97999954223633,48.97999954223633,726357000
21
+ 2020-02-06,46.66133117675781,53.05533218383789,45.79999923706055,49.930667877197266,49.930667877197266,598212000
22
+ 2020-02-07,48.70333480834961,51.31666564941406,48.66666793823242,49.871334075927734,49.871334075927734,255952500
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+ 2020-02-10,53.33333206176758,54.66600036621094,50.15999984741211,51.41866683959961,51.41866683959961,370338000
24
+ 2020-02-11,51.25266647338867,52.23400115966797,50.53333282470703,51.62533187866211,51.62533187866211,175462500
25
+ 2020-02-12,51.858001708984375,52.650001525878906,50.891334533691406,51.15266799926758,51.15266799926758,180337500
26
+ 2020-02-13,49.45600128173828,54.53333282470703,49.0,53.599998474121094,53.599998474121094,394339500
27
+ 2020-02-14,52.4813346862793,54.198001861572266,52.366668701171875,53.33533477783203,53.33533477783203,235405500
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+ 2020-02-21,60.46533203125,60.87066650390625,58.6966667175293,60.06666564941406,60.06666564941406,214722000
32
+ 2020-02-24,55.93333435058594,57.56666564941406,54.813331604003906,55.58599853515625,55.58599853515625,227883000
33
+ 2020-02-25,56.599998474121094,57.106666564941406,52.46666717529297,53.32733154296875,53.32733154296875,259357500
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+ 2020-02-26,52.16666793823242,54.22066879272461,51.740665435791016,51.91999816894531,51.91999816894531,211282500
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+ 2020-02-27,48.66666793823242,49.31800079345703,44.599998474121094,45.266666412353516,45.266666412353516,364158000
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41
+ 2020-03-06,46.0,47.133331298828125,45.61800003051758,46.89866638183594,46.89866638183594,189943500
42
+ 2020-03-09,40.35933303833008,44.20000076293945,40.33333206176758,40.53333282470703,40.53333282470703,256105500
43
+ 2020-03-10,43.96200180053711,44.53333282470703,40.53333282470703,43.02199935913086,43.02199935913086,233916000
44
+ 2020-03-11,42.68000030517578,43.571998596191406,40.866668701171875,42.28200149536133,42.28200149536133,199837500
45
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+ 2022-12-13,174.8699951171875,175.0500030517578,156.91000366210938,160.9499969482422,160.9499969482422,175862700
741
+ 2022-12-14,159.25,161.6199951171875,155.30999755859375,156.8000030517578,156.8000030517578,140682300
742
+ 2022-12-15,153.44000244140625,160.92999267578125,153.27999877929688,157.6699981689453,157.6699981689453,122334500
743
+ 2022-12-16,159.63999938964844,160.99000549316406,150.0399932861328,150.22999572753906,150.22999572753906,139032200
744
+ 2022-12-19,154.0,155.25,145.82000732421875,149.8699951171875,149.8699951171875,139390600
app.py ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import datetime
2
+
3
+ import plotly.graph_objs as go
4
+ import requests
5
+ import streamlit as st
6
+ import yfinance as yf
7
+
8
+ API_URL = "http://127.0.0.1:8000/LSTM_Predict"
9
+
10
+ MIN_DATE = datetime.date(2020, 1, 1)
11
+ MAX_DATE = datetime.date(2022, 12, 31)
12
+
13
+ def main():
14
+ stock_name = st.selectbox(
15
+ "Please choose stock name", ("AAPL", "TSLA", "AMZN", "MSFT")
16
+ )
17
+
18
+ start_date = st.date_input(
19
+ "Start date", min_value=MIN_DATE, max_value=MAX_DATE, value=MIN_DATE
20
+ )
21
+ end_date = st.date_input(
22
+ "End date", min_value=MIN_DATE, max_value=MAX_DATE, value=MAX_DATE
23
+ )
24
+
25
+ if start_date <= end_date:
26
+ st.success(
27
+ f"Selected start date: `{start_date}`\n\nSelected end date:`{end_date}`"
28
+ )
29
+ else:
30
+ st.error("Error: End date must be after start date.")
31
+
32
+ try:
33
+ stock_data = yf.download(stock_name, start=start_date, end=end_date)
34
+ stock_data.reset_index(inplace=True)
35
+
36
+ fig = go.Figure()
37
+ fig.add_trace(go.Scatter(x=stock_data.index, y=stock_data["Close"], name="Close"))
38
+ fig.update_layout(title=f"{stock_name} Stock Price")
39
+ st.plotly_chart(fig)
40
+
41
+ stock_data.to_csv(f"{stock_name}_data.csv", index=False)
42
+
43
+ if st.button("Predict"):
44
+ payload = {"stock_name": stock_name}
45
+ st.write("Sending payload:", payload) # Debugging line
46
+ try:
47
+ response = requests.post(API_URL, json=payload)
48
+ response.raise_for_status()
49
+ predictions = response.json()
50
+ predicted_prices = predictions["prediction"]
51
+
52
+ actual_prices = stock_data["Close"].tolist()
53
+ fig = go.Figure()
54
+ fig.add_trace(
55
+ go.Scatter(x=stock_data.index, y=actual_prices, name="Actual")
56
+ )
57
+ fig.add_trace(
58
+ go.Scatter(
59
+ x=stock_data.index[-len(predicted_prices):],
60
+ y=predicted_prices,
61
+ name="Predicted",
62
+ )
63
+ )
64
+ fig.update_layout(title=f"{stock_name} Stock Price Prediction")
65
+ st.plotly_chart(fig)
66
+ except requests.exceptions.HTTPError as e:
67
+ st.error(f"HTTP error occurred: {e}")
68
+ st.write(response.json()) # Print detailed error message from FastAPI
69
+ except Exception as e:
70
+ st.error(f"An error occurred while downloading stock data: {e}")
71
+
72
+ if __name__ == "__main__":
73
+ main()