Add app file

app.py

@@ -0,0 +1,93 @@
+import random
+import os
+import re
+from langchain_nvidia_ai_endpoints import ChatNVIDIA
+
+# Set environmental variables
+nvidia_api_key = os.getenv("NVIDIA_KEY")
+
+# Initialize ChatNVIDIA for text generation
+client = ChatNVIDIA(
+    model="meta/llama-3.1-8b-instruct",
+    api_key=nvidia_api_key,
+    temperature=0.5,
+    top_p=0.85,
+    max_tokens=80
+)
+
+# Get the absolute path of the script's directory
+script_dir = os.path.dirname(os.path.abspath(__file__))
+phrases_file = os.path.join(script_dir, "phrases.txt")
+
+# Read phrases from the text file
+with open(phrases_file, "r") as f:
+    lines = f.readlines()
+
+def clean_response(response_text):
+    # Join the response list into a single string
+    response_end = " ".join(response_text).strip()
+
+    # Replace multiple spaces with a single space
+    response_end = re.sub(r'\s+', ' ', response_end)  # Collapses multiple spaces into one
+
+    # Attempt to fix improper word splitting only
+    # This regex finds single-letter space single-letter cases more precisely and joins them
+    response_end = re.sub(r'(\b\w)\s+(\w\b)', r'\1\2', response_end, flags=re.UNICODE)
+
+    response_end = re.sub(r'\s*([,.!?])\s*', r'\1 ', response_end)
+
+    # Remove any extraneous newlines or extra spaces left over
+    response_end = response_end.replace("\n", " ").replace("  ", " ")
+
+    # Trim any leading or trailing spaces
+    response_end = response_end.strip()
+    
+    return response_text
+
+def generate(starting_text):
+    for count in range(6):
+        seed = random.randint(100, 1000000)
+        random.seed(seed)
+    
+        # If the text field is empty
+        if starting_text == "":
+            starting_text = lines[random.randrange(0, len(lines))].strip()
+            starting_text = re.sub(r"[,:\-–.!;?_]", '', starting_text)
+    
+        # Format messages for the client
+        messages = [
+            {"role": "system", "content": "Create a vivid and imaginative MidJourney prompt for image generation. Focus on descriptive language, colors, and setting to evoke a specific visual scene. Avoid storytelling; focus on visual elements."},
+            {"role": "user", "content": starting_text}
+        ]
+
+        # Stream response from LLaMA
+        response = client.stream(messages)
+
+        # Buffer to accumulate response chunks
+        buffer = []
+        for chunk in response:
+            if chunk.content:
+                resp = chunk.content.strip()
+                if resp != starting_text:
+                    buffer.append(resp)
+
+        # Join the buffered chunks into a single string
+        response_text = " ".join(buffer).strip()
+
+        # Clean up the response text
+        response_end = clean_response(response_text)
+
+        # Print only the final response if it's not empty
+        if response_end != "":
+            return response_end
+
+# Example usage
+if __name__ == "__main__":
+    # Get user input for the starting text
+    user_input = input("Enter a starting text for the Midjourney prompt: ")
+
+    # Generate the prompt
+    generated_prompt = generate(user_input)
+
+    # Print the generated prompt
+    print(f"Generated Prompt:\n{generated_prompt}")