Merge pull request #1 from Code-Byte/kartik31-patch-1

Add files via upload

Author: kartik31

classify.py

@@ -0,0 +1,84 @@
+from __future__ import print_function, division
+from flask import Flask , redirect , url_for , request , render_template , jsonify , json
+import pandas as pd 
+import os
+import random
+from collections import Counter
+import numpy as np
+import tensorflow as tf
+import json
+import sys 
+
+
+
+app = Flask(__name__)
+
+
+# @app.route("/predict" , methods=['POST'])
+@app.route('/'  , methods=['POST'])
+
+def hello():
+    data = request.data
+    # query = str(json_)
+    print(data)
+    imagePath = data
+    modelFullPath = './output_graph.pb'
+    labelsFullPath = './output_labels.txt'
+
+
+
+
+
+    def create_graph():
+        """Creates a graph from saved GraphDef file and returns a saver."""
+        # Creates graph from saved graph_def.pb.
+        with tf.gfile.FastGFile(modelFullPath, 'rb') as f:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(f.read())
+            _ = tf.import_graph_def(graph_def, name='')
+    def run_inference_on_image():
+        answer = None
+
+        if not tf.gfile.Exists(imagePath):
+            tf.logging.fatal('File does not exist %s', imagePath)
+            return answer
+
+        image_data = tf.gfile.FastGFile(imagePath, 'rb').read()
+
+        # Creates graph from saved GraphDef.
+        create_graph()
+
+        with tf.Session() as sess:
+
+            softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
+            predictions = sess.run(softmax_tensor,
+                                {'DecodeJpeg/contents:0': image_data})
+            predictions = np.squeeze(predictions)
+
+            top_k = predictions.argsort()[-1:][::-1]  # Getting top 5 predictions
+            f = open(labelsFullPath, 'rb')
+            lines = f.readlines()
+            labels = [str(w).replace("\n", "") for w in lines]
+            for node_id in top_k:
+                human_string = labels[node_id]
+                score = predictions[node_id]
+                # return '%s (score = %.5f)' % (human_string, score)
+
+            answer = labels[top_k[0]]
+            return answer
+    answer = run_inference_on_image()
+    return '%s' % answer
+
+
+
+    # if __name__ == '__main__':
+    
+    
+
+
+if __name__ == '__main__':
+    # imagePath = './image1.jpg'
+    modelFullPath = './output_graph.pb'
+    labelsFullPath = './output_labels.txt'
+   # app.run(debug = True)
+    app.run('0.0.0.0' , 5000)