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app.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas_datareader as data
+import plotly.express as px
+import streamlit as st
+import requests
+from streamlit_lottie import st_lottie
+from keras.models import load_model
+
+st.set_page_config(
+    page_title=" Stonks Trends Prediction", #The page title, shown in the browser tab.(should be Placement Details)
+    initial_sidebar_state="auto", #The way sidebar should start out. Auto shows it in desktop.
+    page_icon=":computer:", #The page favicon. Use the computer emoji
+    layout="wide", #The way page content should be laid out. "wide" uses the entire screen.
+    menu_items={ #Configure the menu that appears on the top-right side of this app.
+            'About': 'https://www.linkedin.com/in/harsh-kashyap-79b87b193/', #A markdown string to show in the About dialog. Used my linkedIn id
+     }
+)
+
+from datetime import date
+from datetime import timedelta
+today = date.today()
+# Yesterday date
+yesterday = today - timedelta(days = 1)
+start='2008-01-01'
+end=yesterday;
+
+st.title(":computer:  Stock Market Predictor") #Title heading of the page
+st.markdown("##") 
+
+
+
+st.subheader("Enter Stock Ticker")
+user_input=st.text_input('','AAPL')
+df=data.DataReader(user_input,'yahoo',start,end)
+date=df.index
+
+#Checks if which parameters in hsc_s which is named as branch in sidebar is checked or not and display results accordingly
+def load_lottieurl(url: str):
+    r = requests.get(url) #Make a request to a web page, and return the status code:
+    if r.status_code != 200: #200 is the HTTP status code for "OK", a successful response.
+        return None
+    return r.json() #return the animated gif
+
+left_column, right_column = st.columns(2) #Columns divided into two parts
+with left_column:
+    dashboard1 = load_lottieurl("https://assets10.lottiefiles.com/packages/lf20_kuhijlvx.json") #get the animated gif from file
+    st_lottie(dashboard1, key="Dashboard1", height=400) #change the size to height 400
+with right_column:
+    dashboard2 = load_lottieurl("https://assets10.lottiefiles.com/packages/lf20_i2eyukor.json") #get the animated gif from file
+    st_lottie(dashboard2, key="Dashboard2", height=400) #change the size to height 400
+
+
+st.markdown("""---""")
+#Describing data 
+st.subheader('Data from 2008 to '+str(end.year))
+st.write(df.describe())
+
+st.markdown("""---""")
+#Visualisations
+st.subheader("Closing Price vs Time Chart of "+str(user_input)) #Header
+#plot a line graph
+fig_line = px.line(
+    df,  
+    x = df.index,  
+    y = "Close", 
+    width=1400, #width of the chart
+    height=750, #height of the chart
+)
+#remove the background of the back label
+fig_line.update_layout(
+    plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
+    xaxis=(dict(showgrid=False)) #dont show the grid
+)
+#plot the chart
+st.plotly_chart(fig_line, use_container_width=True)
+st.markdown("""---""")
+
+
+st.subheader("Closing Price vs Time with 100MA of "+str(user_input)) #Header
+ma100=df.Close.rolling(100).mean()
+#plot a line graph
+fig_line = px.line( 
+    ma100,
+    x = df.index,  
+    y = ma100, 
+    width=1400, #width of the chart
+    height=750, #height of the chart
+)
+#remove the background of the back label
+fig_line.update_layout(
+    plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
+    xaxis=(dict(showgrid=False)) #dont show the grid
+)
+#plot the chart
+st.plotly_chart(fig_line, use_container_width=True)
+st.markdown("""---""")
+
+
+st.subheader("Closing Price vs Time with 1 year moving average of  "+str(user_input)) #Header
+ma365=df.Close.rolling(365).mean()
+#plot a line graph
+fig_line = px.line( 
+    ma365,
+    x = df.index,  
+    y = ma365, 
+    width=1400, #width of the chart
+    height=750, #height of the chart
+)
+#remove the background of the back label
+fig_line.update_layout(
+    plot_bgcolor="rgba(0,0,0,0)",  #rgba means transparent
+    xaxis=(dict(showgrid=False)) #dont show the grid
+)
+#plot the chart
+st.plotly_chart(fig_line, use_container_width=True)
+st.markdown("""---""")
+
+
+
+#Splitting data into training and testing
+
+data_training= pd.DataFrame(df['Close'][0:int(len(df)*0.7)])
+data_testing= pd.DataFrame(df['Close'][int(len(df)*0.7):int(len(df))])
+ydate= date[int(len(df)*0.7):int(len(df))]
+print(data_training.shape)
+print(data_testing.shape)
+
+#normalising data
+from sklearn.preprocessing import MinMaxScaler
+scaler=MinMaxScaler(feature_range=(0,1))
+
+dataset_train = scaler.fit_transform(data_training)
+dataset_test = scaler.transform(data_testing)
+
+def create_dataset(df):
+    x = []
+    y = []
+    for i in range(50, df.shape[0]):
+        x.append(df[i-50:i, 0])
+        y.append(df[i, 0])
+    x = np.array(x)
+    y = np.array(y)
+    return x,y
+
+#Creating dataset
+x_train, y_train = create_dataset(dataset_train)
+x_test, y_test = create_dataset(dataset_test)
+
+x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
+x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+
+#Load my model
+model=load_model('stock_prediction.h5')
+
+predictions = model.predict(x_test)
+predictions = scaler.inverse_transform(predictions)
+y_test_scaled = scaler.inverse_transform(y_test.reshape(-1, 1))
+
+cydate=ydate[50:]
+st.markdown("""---""")
+st.subheader("Actual Vs Predicted Price Graph for "+user_input)
+fig, ax = plt.subplots(figsize=(16,8))
+ax.set_facecolor('#000041')
+ax.plot(cydate,y_test_scaled, color='red', label='Original price')
+plt.plot(cydate,predictions, color='cyan', label='Predicted price')
+plt.xlabel("Date")
+plt.ylabel("Price")
+plt.title("Stocks for the company "+str(user_input))
+plt.legend()
+st.pyplot(fig)
+
+st.markdown("""---""")

requirements.txt

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+absl-py==1.2.0
+aiofiles==22.1.0
+altair==4.2.0
+anyio==3.6.1
+appnope==0.1.3
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+astroid==2.11.7
+asttokens==2.0.5
+astunparse==1.6.3
+attrs==22.1.0
+Babel==2.10.3
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bert-extractive-summarizer==0.4.2
+bleach==5.0.1
+blinker==1.5
+blis==0.7.9
+boto3==1.26.18
+botocore==1.29.18
+cached-property==1.5.2
+cachetools==5.2.0
+catalogue==2.0.8
+certifi==2022.6.15
+cffi==1.15.1
+charset-normalizer==2.1.0
+ci-info==0.3.0
+click==8.1.3
+commonmark==0.9.1
+confection==0.0.3
+configobj==5.0.6
+configparser==5.3.0
+cycler==0.11.0
+cymem==2.0.7
+debugpy==1.6.2
+decorator==5.1.1
+defusedxml==0.7.1
+dill==0.3.5.1
+docopt==0.6.2
+docx==0.2.4
+entrypoints==0.4
+etelemetry==0.3.0
+executing==0.8.3
+fastjsonschema==2.16.1
+filelock==3.8.0
+fitz==0.0.1.dev2
+Flask==2.2.2
+Flask-SQLAlchemy==3.0.2
+flatbuffers==1.12
+fonttools==4.34.4
+frontend==0.0.3
+future==0.18.2
+gast==0.4.0
+gitdb==4.0.9
+GitPython==3.1.27
+google-auth==2.9.1
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+greenlet==2.0.1
+grpcio==1.47.0
+gTTS==2.2.4
+h11==0.14.0
+h5py==3.7.0
+httplib2==0.21.0
+huggingface-hub==0.11.1
+idna==3.3
+importlib-metadata==4.12.0
+inflection==0.5.1
+ipykernel==6.15.1
+ipython==8.4.0
+ipython-genutils==0.2.0
+ipywidgets==7.7.1
+isodate==0.6.1
+isort==5.10.1
+itsdangerous==2.1.2
+jedi==0.18.1
+Jinja2==3.1.2
+jmespath==1.0.1
+joblib==1.1.0
+json5==0.9.10
+jsonschema==4.9.1
+jupyter-client==7.3.4
+jupyter-core==4.11.1
+jupyter-server==1.18.1
+jupyterlab==3.4.5
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==1.1.1
+jupyterlab_server==2.15.1
+keras==2.9.0
+Keras-Preprocessing==1.1.2
+kiwisolver==1.4.3
+langcodes==3.3.0
+lazy-object-proxy==1.7.1
+libclang==14.0.6
+looseversion==1.0.2
+lxml==4.9.1
+Markdown==3.4.1
+MarkupSafe==2.1.1
+matplotlib==3.5.2
+matplotlib-inline==0.1.3
+mccabe==0.7.0
+mistune==0.8.4
+murmurhash==1.0.9
+mypy-extensions==0.4.3
+mysql-connector==2.2.9
+mysql-connector-python-rf==2.2.2
+nbclassic==0.4.3
+nbclient==0.6.6
+nbconvert==6.5.0
+nbformat==5.4.0
+nest-asyncio==1.5.5
+networkx==2.8.8
+nibabel==4.0.2
+nipype==1.8.5
+nltk==3.7
+notebook==6.4.12
+notebook-shim==0.1.0
+numpy==1.23.1
+oauthlib==3.2.0
+opencv-python==4.6.0.66
+opt-einsum==3.3.0
+packaging==21.3
+pandas==1.4.3
+pandas-datareader==0.10.0
+pandocfilters==1.5.0
+parso==0.8.3
+passlib==1.7.4
+pathlib==1.0.1
+pathy==0.10.0
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.2.0
+pipreqs==0.4.11
+platformdirs==2.5.2
+plotly==5.9.0
+preshed==3.0.8
+prometheus-client==0.14.1
+prompt-toolkit==3.0.30
+protobuf==3.19.4
+prov==2.0.0
+psutil==5.9.1
+ptyprocess==0.7.0
+pure-eval==0.2.2
+py4j==0.10.9.5
+pyarrow==9.0.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.21
+pydantic==1.10.2
+pydeck==0.7.1
+pydot==1.4.2
+Pygments==2.12.0
+pylint==2.14.4
+Pympler==1.0.1
+PyMuPDF==1.21.0
+pyparsing==3.0.9
+pyrsistent==0.18.1
+pyspark==3.3.0
+pytesseract==0.3.10
+python-dateutil==2.8.2
+python-docx==0.8.11
+pytils==0.4.1
+pytz==2022.1
+pytz-deprecation-shim==0.1.0.post0
+pyxnat==1.5
+PyYAML==6.0
+pyzmq==23.2.0
+qrcode==7.3.1
+rdflib==6.2.0
+regex==2022.10.31
+requests==2.28.1
+requests-oauthlib==1.3.1
+rich==12.5.1
+rsa==4.9
+s3transfer==0.6.0
+scikit-learn==1.1.1
+scipy==1.9.0
+seaborn==0.11.2
+semver==2.13.0
+Send2Trash==1.8.0
+sentence-transformers==2.2.2
+sentencepiece==0.1.97
+simplejson==3.18.0
+six==1.16.0
+smart-open==5.2.1
+smmap==5.0.0
+sniffio==1.2.0
+soupsieve==2.3.2.post1
+spacy==3.4.3
+spacy-legacy==3.0.10
+spacy-loggers==1.0.3
+SQLAlchemy==1.4.44
+sqlalchemy-orm==1.2.3
+srsly==2.4.5
+stack-data==0.3.0
+starlette==0.23.0
+streamlit==1.11.1
+streamlit-lottie==0.0.3
+tenacity==8.0.1
+tensorboard==2.9.1
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorflow==2.9.1
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+termcolor==1.1.0
+terminado==0.15.0
+thinc==8.1.5
+threadpoolctl==3.1.0
+tinycss2==1.1.1
+tokenizers==0.13.2
+toml==0.10.2
+tomli==2.0.1
+tomlkit==0.11.1
+tools==0.1.9
+toolz==0.12.0
+torch==1.13.0
+torchvision==0.14.0
+tornado==6.2
+tqdm==4.64.1
+traitlets==5.3.0
+traits==6.3.2
+transformers==4.24.0
+typer==0.7.0
+typing-inspect==0.8.0
+typing_extensions==4.3.0
+tzdata==2022.1
+tzlocal==4.2
+urllib3==1.26.11
+uvicorn==0.20.0
+validators==0.20.0
+wasabi==0.10.1
+wcwidth==0.2.5
+webencodings==0.5.1
+websocket-client==1.4.0
+Werkzeug==2.2.2
+widgetsnbextension==3.6.1
+wrapt==1.14.1
+yarg==0.1.9
+zipp==3.8.1

stock_prediction.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:230bec19c7ac3b71d78e29f788fa7155b55e6c5836872c26c1c809de4c14f463
+size 3195144