Update app.py

app.py

@@ -36,25 +36,30 @@ def infer1(starting_text):
         return response_end
 
 def infer2(starting_text):
-    seed = random.randint(100, 1000000)
-    set_seed(seed)
-
-    if starting_text == "":
-        starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
-
-    response = gpt2_pipe2(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
-    response_list = []
-    for x in response:
-        resp = x['generated_text'].strip()
-        if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
-            response_list.append(resp+'\n')
-
-    response_end = "\n".join(response_list)
-    response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
-    response_end = response_end.replace("<", "").replace(">", "")
-
-    if response_end != "":
-        return response_end
+    for count in range(6):
+        seed = random.randint(100, 1000000)
+        set_seed(seed)
+    
+        # If the text field is empty
+        if starting_text == "":
+            starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
+            starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
+            print(starting_text)
+    
+        response = gpt2_pipe2(starting_text, max_length=random.randint(60, 90), num_return_sequences=8)
+        response_list = []
+        for x in response:
+            resp = x['generated_text'].strip()
+            if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
+                response_list.append(resp)
+    
+        response_end = "\n".join(response_list)
+        response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
+        response_end = response_end.replace("<", "").replace(">", "")
+        if response_end != "":
+            return response_end
+        if count == 5:
+            return response_end
 
 def infer3(prompt, negative, steps, scale, seed):
     generator = torch.Generator(device='cpu').manual_seed(seed)