Merge remote-tracking branch 'refs/remotes/origin/main'

.github/workflows/main.yml

@@ -0,0 +1,25 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Add remote
+        env:
+          HG: ${{secrets.HG}}
+        run: git remote add space https://chibao24:[email protected]/spaces/chibao24/demo2
+        
+      - name: Push to hub
+        env:
+          HG: ${{secrets.HG}}
+        run: git push --force https://chibao24:[email protected]/spaces/chibao24/demo2 main
+          

app.py

@@ -1,20 +1,23 @@
 from transformers import pipeline
 import gradio as gr
 
+<<<<<<< HEAD
 # I add a comt
 # Initialize the summarization model
 model = pipeline("summarization")
 
+=======
+
+model = pipeline(
+    "summarization",
+)
+>>>>>>> refs/remotes/origin/main
 
 def predict(prompt):
     summary = model(prompt)[0]["summary_text"]
     return summary
 
 
-# Create a Gradio interface
-interface = gr.Interface(fn=predict, inputs=gr.Textbox(placeholder="Enter text to summarize", lines=4), outputs="text")
-
-
-# Launch the interface
-interface.launch()
-
+# create an interface for the model
+with gr.Interface(predict, "textbox", "text") as interface:
+    interface.launch()

hello.py

@@ -1 +0,0 @@
-print ("hello")

sample_classification.py

@@ -1,25 +0,0 @@
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-
-# Generate random data
-X = np.random.randint(0, 100, (1000, 2))
-y = np.random.randint(0, 2, 1000)
-
-# Split the data into training and test sets
-X_train = X[:750]
-y_train = y[:750]
-X_test = X[750:]
-y_test = y[750:]
-
-# Create the classifier
-clf = LogisticRegression()
-
-# Train the classifier
-clf.fit(X_train, y_train)
-
-# Predict the labels of the test set
-y_pred = clf.predict(X_test)
-
-# Evaluate the accuracy of the classifier
-accuracy = np.mean(y_pred == y_test)
-print("Accuracy:", accuracy)