Create app.py

app.py

@@ -1,19 +1,68 @@
-import gradio as gr
-
-# Define your AI model or function here
-def my_ai_model(input_text):
-    # Your AI logic here (for now it just repeats the input)
-    result = f"You said: {input_text}"
-    return result
-
-# Create a Gradio interface
-interface = gr.Interface(
-    fn=my_ai_model,
-    inputs="text",
-    outputs="text",
-    title="My Simple AI",
-    description="This AI responds to your input!"
-)
-
-# Launch the interface
-interface.launch()
+from flask import Flask, request, jsonify
+import numpy as np
+import tensorflow as tf
+import torch
+from transformers import pipeline
+import cv2
+import os
+
+app = Flask(__name__)
+
+# Example model loading (you can replace this with your actual models)
+# TensorFlow model
+tf_model = tf.keras.models.load_model('path_to_your_tf_model')
+
+# PyTorch model
+torch_model = torch.load('path_to_your_torch_model')
+torch_model.eval()
+
+# Hugging Face Transformers pipeline (e.g., for text generation)
+text_gen_pipeline = pipeline("text-generation", model="gpt2")
+
+@app.route('/')
+def index():
+    return "Welcome to the AI app! Endpoints are ready."
+
+# Endpoint to make predictions using TensorFlow model
+@app.route('/predict_tf', methods=['POST'])
+def predict_tf():
+    data = request.json
+    input_data = np.array(data['input'])
+    prediction = tf_model.predict(input_data)
+    return jsonify({"prediction": prediction.tolist()})
+
+# Endpoint to make predictions using PyTorch model
+@app.route('/predict_torch', methods=['POST'])
+def predict_torch():
+    data = request.json
+    input_data = torch.tensor(data['input'])
+    prediction = torch_model(input_data)
+    return jsonify({"prediction": prediction.detach().numpy().tolist()})
+
+# Text generation using Hugging Face Transformers
+@app.route('/generate_text', methods=['POST'])
+def generate_text():
+    data = request.json
+    prompt = data['prompt']
+    result = text_gen_pipeline(prompt, max_length=100, num_return_sequences=1)
+    return jsonify({"generated_text": result[0]['generated_text']})
+
+# Endpoint to process an image (using OpenCV)
+@app.route('/process_image', methods=['POST'])
+def process_image():
+    if 'file' not in request.files:
+        return "No file found", 400
+    file = request.files['file']
+    img = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)
+    # Example processing: convert image to grayscale
+    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # Save the processed image
+    processed_path = 'processed_image.jpg'
+    cv2.imwrite(processed_path, gray_img)
+    return jsonify({"message": "Image processed", "file_path": processed_path})
+
+# Future abilities and additional features can be added here
+
+if __name__ == '__main__':
+    # Run the app
+    app.run(host='0.0.0.0', port=5000, debug=True)