Update app.py

app.py

@@ -1,65 +1,24 @@
-import os
-import random
-import string
-from django.apps import AppConfig
-from django.conf import settings
-from jinja2 import Environment, FileSystemLoader
-import openai
-
-class AppNameConfig(AppConfig):
-    default_auto_field = 'django.db.models.BigAutoField'
-    name = 'app_name'
-
-    def ready(self):
-        # import models, functions, and libraries here
-        from . import models, functions, libraries
-
-        # idea-to-app logic
-        if settings.IDEA_TO_APP:
-            # generate a unique name for the new model
-            model_name = ''.join(random.choices(string.ascii_lowercase + string.digits, k=10))
-            # merge the selected models and functions here
-            # and save it as a new GGuf model
-            gguf_model = functions.merge_models(models.CodeModel, models.ImageModel, 'function1', 'function3')
-            # save the GGuf model under the unique name
-            gguf_model.save(model_name)
-            # return the merged model for preview/demo
-            settings.IDEA_TO_APP_PREVIEW = gguf_model
-
-from django.db import models
-
-class CodeModel(models.Model):
-    name = models.CharField(max_length=100)
-    code = models.TextField()
-
-class ImageModel(models.Model):
-    name = models.CharField(max_length=100)
-    image = models.ImageField(upload_to='images/')
-
-def merge_models(model1, model2, function1, function3):
-    # merge the selected models and functions here
-    model1_objects = model1.objects.all()
-    model2_objects = model2.objects.all()
-    merged_objects = []
-    for obj1 in model1_objects:
-        obj2 = model2_objects.filter(name=obj1.name).first()
-        if obj2:
-            merged_obj = {
-                'name': obj1.name,
-                'code': function1(obj1.code),
-                'image': function3(obj2.image),
-            }
-            merged_objects.append(merged_obj)
-    return merged_objects
-
-import openai
-import jinja2
-
-def function1(code):
-    # translate natural language to executable code here
-    # using the OpenAI API
-    openai.api_key = 'YOUR_OPENAI_API_KEY'
-    response = openai.Completion.create(
-        engine='code-davinci-002',
-        prompt=f'Translate this Python code to executable code: {code}'\
-    )
+from flask import Flask, request, jsonify
+from huggingface_hub import HfApi
+
+app = Flask(__name__)
+api = HfApi()
+
+@app.route('/search_datasets', methods=['GET'])
+def search_datasets():
+    query = request.args.get('query')
+    datasets = api.list_datasets(search=query, full=True)
+    return jsonify(datasets)
+
+@app.route('/run_inference', methods=['POST'])
+def run_inference():
+    model_id = request.json['model_id']
+    inputs = request.json['inputs']
+    # Assuming the model is compatible with the pipeline API
+    from transformers import pipeline
+    model_pipeline = pipeline(task="text-generation", model=model_id)
+    results = model_pipeline(inputs)
+    return jsonify(results)
+
+if __name__ == '__main__':
+    app.run(debug=True)