added font

app.py

@@ -10,10 +10,14 @@ import cv2
 import numpy as np
 
 
-def predict(action,image, text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+def predict(action,image, text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space):
-    def titleonImage(action,image, text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+    def titleonImage(action,image, text_in_image, words_line,font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space):
+        font_dict = {'normal size sans-serif':cv2.FONT_HERSHEY_SCRIPT_COMPLEX, 'small size sans-serif':cv2.FONT_HERSHEY_PLAIN,
+                    'normal size sans-serif (complexity +1)': cv2.FONT_HERSHEY_DUPLEX, 'normal size sans-serif (complexity +2)': cv2.FONT_HERSHEY_COMPLEX ,
+                    'normal size sans-serif (complexity +3)': cv2.FONT_HERSHEY_TRIPLEX, 'small size sans-serif (complexity +2)': cv2.FONT_HERSHEY_COMPLEX_SMALL,
+                    'hand-writing style font': cv2.FONT_HERSHEY_SCRIPT_SIMPLEX, 'hand-writing style font (complexity +1)': cv2.FONT_HERSHEY_SCRIPT_COMPLEX }
 
         inp = text_in_image
         string = ""
@@ -30,7 +34,7 @@ def predict(action,image, text_in_image, words_line, model, url_or_text, prompt,
         for i in text_in_image.split("\n"):
             image = ps.putBText(image, i, text_offset_x = int(x_cor),
                                 text_offset_y = int(y_cor)+y_gap, vspace = 5,
-                                hspace = 5, font_scale = int(font_size),
+                                hspace = 5, font_scale = int(font_size), font = font_dict[font],
                                 background_RGB = ast.literal_eval(backgound_RGB),
                                 text_RGB = ast.literal_eval(text_RGB), thickness = int(thickness),
                                 alpha = float(opacity))
@@ -44,7 +48,7 @@ def predict(action,image, text_in_image, words_line, model, url_or_text, prompt,
             article.parse()
             text = article.text
         return text
-    def getTitle(text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+    def getTitle(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space):
         response = openai.Completion.create(
             model=model,
@@ -57,7 +61,7 @@ def predict(action,image, text_in_image, words_line, model, url_or_text, prompt,
         ).choices[0].text
         return response
     
-    def getSummary(text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+    def getSummary(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space):
         summary = openai.Completion.create(
             model=model,
@@ -73,35 +77,35 @@ def predict(action,image, text_in_image, words_line, model, url_or_text, prompt,
 #     text_in_image = "title was not asked"   --> only need in summary 
     summary = "summary was not asked"
     if action == "title on Image":
-        image_final = titleonImage(action,image, text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+        image_final = titleonImage(action,image, text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
     
     if action == "title on image from url/text":
         url_or_text = getText(url_or_text)
-        text_in_image = getTitle(text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+        text_in_image = getTitle(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
         
-        image_final =  titleonImage(action,image, text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+        image_final =  titleonImage(action,image, text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
     
     if action == "title on image and summary from url/text":
         url_or_text = getText(url_or_text)
-        text_in_image = getTitle(text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+        text_in_image = getTitle(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-        summary = getSummary(text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+        summary = getSummary(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
-        image_final =  titleonImage(action,image, text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+        image_final =  titleonImage(action,image, text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
         
     if action == "title from url/text":
         url_or_text = getText(url_or_text)
-        text_in_image = getTitle(text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+        text_in_image = getTitle(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
     
     if action == "summary from url/text":
         url_or_text = getText(url_or_text)
         text_in_image = "title was not asked"
-        summary = getSummary(text_in_image, words_line, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
+        summary = getSummary(text_in_image, words_line, font, model, url_or_text, prompt, summary_prompt, temperature, max_tokens, x_cor, y_cor,
             font_size, backgound_RGB, text_RGB, thickness, opacity, line_space)
     return  image_final, text_in_image, summary
 
@@ -115,6 +119,11 @@ intr = gr.Interface(predict,
                      "image",
                      gr.Textbox(value="Text was not given", label = "Text you want to see in the image"),
                      gr.Number(value=5, label = "Number of words in a line"),
+                     gr.Dropdown(['normal size sans-serif', 'small size sans-serif',
+                                 'normal size sans-serif (complexity +1)', 'normal size sans-serif (complexity +2)',
+                                 'normal size sans-serif (complexity +3)', 'small size sans-serif (complexity +2)',
+                                 'hand-writing style font', 'hand-writing style font (complexity +1)'],
+                                label = "Font for the text", value = 'normal size sans-serif'),
                      gr.Dropdown(["text-curie-001", "text-davinci-003"], value = "text-curie-001", label = "GPT3- Model"),
                      gr.Textbox(value="https://www.datasciencecentral.com/will-chatgpt-make-fraud-easier/", label = "Provide blog url or directly input the text for better results"),
                      gr.Textbox(value="Title for instagram post based on above article", label= "Prompt for generating the title"),