Update README.md

README.md

@@ -6,4 +6,76 @@ language:
 - en
 base_model:
 - black-forest-labs/FLUX.1-dev
----
+---
+#This prompt is from message 2. #The goal is to generate 100 messages per prompt. 
+
+prompt2 = "Vaping is risky"
+
+#Below, we specify to use pytorch machine learning framework. 
+#You can also choose Tensorflow, but we use Pytorch here.
+
+inputs = tokenizer(prompt2, return_tensors="pt")
+---
+#We generate 50 messages each time due to restrictions in Ram storage. 
+
+sample_outputs = bloom.generate(inputs["input_ids"],
+                       temperature = 0.7,
+                       max_new_tokens = 60,
+                       do_sample=True,
+                       top_k=40, 
+                       top_p=0.9,
+                       num_return_sequences=50
+                      )
+
+print("Output:\n" + 100 * '-')
+messages = []
+for i, sample_output in enumerate(sample_outputs):
+  generated_messages = tokenizer.decode(sample_output, skip_special_tokens=True)
+  print("{}: {}".format(i, generated_messages))
+  messages.append(generated_messages)
+
+print(messages)
+---
+#We save the AI-generated messages to google drive. 
+
+AI_messages = pd.DataFrame(messages, columns = ['tweet'])
+AI_messages.to_csv('Vaping is risky1.csv', index = False)
+---
+#Then generate another 50 messages with prompt1 and then save to google drive.
+
+AI_messages = pd.DataFrame(messages, columns = ['tweet'])
+AI_messages.to_csv('Vaping is risky2.csv', index = False)
+---
+#This prompt is from message 3. #The goal is to generate 100 messages per prompt. 
+
+prompt3 = "Vapes and e-cigarettes increase your risk"
+
+#Below, we specify to use pytorch machine learning framework. 
+#You can also choose Tensorflow, but we use Pytorch here.
+
+inputs = tokenizer(prompt3, return_tensors="pt")
+---
+#We generate 50 messages each time due to restrictions in Ram storage. 
+
+sample_outputs = bloom.generate(inputs["input_ids"],
+                       temperature = 0.7,
+                       max_new_tokens = 60,
+                       do_sample=True,
+                       top_k=40, 
+                       top_p=0.9,
+                       num_return_sequences=50
+                      )
+
+print("Output:\n" + 100 * '-')
+messages = []
+for i, sample_output in enumerate(sample_outputs):
+  generated_messages = tokenizer.decode(sample_output, skip_special_tokens=True)
+  print("{}: {}".format(i, generated_messages))
+  messages.append(generated_messages)
+
+print(messages)
+---
+#We save the AI-generated messages to google drive. 
+
+AI_messages = pd.DataFrame(messages, columns = ['tweet'])
+AI_messages.to_csv('Vapes and e-cigarettes increase your risk1.csv', index = False)