First working model

BA-test-2.csv

@@ -0,0 +1,413 @@
+Date,Open,High,Low,Close,Adj Close,Volume
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BA-test-3.csv

@@ -0,0 +1,119 @@
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app.py

@@ -1,7 +1,39 @@
 import gradio as gr
+from fastai.data.all import *
+from fastai.vision.all import *
+import pandas as pd
 
-def greet(name):
-    return "Hello " + name + "!!"
+learn_fc = load_learner('fc_model.pkl')
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch() 
+class MyDataset:
+    """Great for big datasets and long sequencies"""
+    def __init__(self, df, features, target, look_back=1, prediction_horizon=1): 
+        self.df, self.look_back, self.prediction_horizon = df, look_back, prediction_horizon
+        self.features, self.target = features, target
+    def __len__(self): 
+        n = len(self.df)-self.look_back+1-self.prediction_horizon
+        return n
+    def new_empty(self): return self[0]
+    def __getitem__(self, i):
+        p = self.look_back
+        q = self.prediction_horizon
+        X = self.df.iloc[i:(i+p)][self.features].values
+        y = self.df.iloc[(i+p):(i+p + q)][self.target].values
+        return tensor(X), tensor(y)
+
+def predict(df):
+    df.set_index('Date', inplace=True)
+    df = (df - train_mean) / train_std
+    test_ds = MyDataset(df, feature_selected, label_selected, look_back, prediction_horizon)
+    pred,_,_ = learn_fc.predict(test_ds[len(test_ds)-1])
+    fig = plt.figure()
+    plt.plot(pred)
+    return fig
+
+title = "Predict time series"
+description = "Predict time series"
+# article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['BA-test.csv', 'BA-test-2.csv', 'BA-test-3.csv']
+
+iface = gr.Interface(fn=predict, inputs=gr.inputs.Timeseries(x=None, y=['Date','Open','High','Low','Close','Adj Close','Volume']), outputs=gr.outputs.Plot(type='matplotlib'), examples=examples)
+iface.launch()

requirements.txt

@@ -0,0 +1 @@
+fastai