Update app.py

app.py

@@ -1,44 +1,203 @@
-import os
-import requests
 import gradio as gr
+import os
+from huggingface_hub import HfApi
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Hugging Face token and space info
+HF_TOKEN = "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"
+HF_SPACE = "Hardik5456/Wan2.1playground"
+
+# Initialize Hugging Face API
+api = HfApi(token=HF_TOKEN)
+
+# We'll use Mistral-7B as it's a good balance of quality and performance
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.2"
+
+# Load configuration content
+with open("spectral_satya_gpt_configuration.md", "r") as f:
+    config_content = f.read()
+
+with open("spectral_satya_book_integration.md", "r") as f:
+    book_integration = f.read()
+
+with open("spectral_satya_prompt_examples.md", "r") as f:
+    prompt_examples = f.read()
+
+# System prompt that incorporates the configuration
+SYSTEM_PROMPT = f"""
+You are Spectral Satya, a specialized AI assistant focused on crafting cinematic reality through expert prompt engineering for AI video generation.
 
-# Retrieve the NVIDIA API key from Hugging Face Space secrets
-NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY")
-if not NVIDIA_API_KEY:
-    raise ValueError("NVIDIA_API_KEY not found in environment variables")
-
-# Function to generate a response using the NVIDIA API
-def generate_response(input_text):
-    # Placeholder NVIDIA API endpoint (replace with the actual endpoint)
-    url = "https://api.nvidia.com/v1/models/llama-3.1-nemotron-ultra-253b-v1/generate"
-    headers = {
-        "Authorization": f"Bearer {NVIDIA_API_KEY}",
-        "Content-Type": "application/json",
-    }
-    data = {
-        "prompt": input_text,
-        "max_tokens": 100,  # Adjust as needed
-        "temperature": 0.7,  # Adjust for creativity/control
-        # Add other parameters as required by the NVIDIA API
-    }
+{config_content}
+
+Your responses should follow the principles and techniques outlined in "The Prompt Engineer's Codex: Crafting Cinematic Reality with AI Video".
+"""
+
+# Initialize tokenizer and model
+try:
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    pipe = pipeline(
+        "text-generation",
+        model=MODEL_NAME,
+        tokenizer=tokenizer,
+        max_new_tokens=1024,
+        temperature=0.7,
+        top_p=0.95,
+        repetition_penalty=1.15
+    )
+except Exception as e:
+    print(f"Error loading model: {e}")
+    # Fallback to a smaller model if the main one fails
+    MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    pipe = pipeline(
+        "text-generation",
+        model=MODEL_NAME,
+        tokenizer=tokenizer,
+        max_new_tokens=512,
+        temperature=0.7,
+        top_p=0.95,
+        repetition_penalty=1.15
+    )
+
+def format_prompt(user_input, chat_history):
+    """Format the prompt for the model with chat history."""
+    messages = []
+    
+    # Add system prompt
+    messages.append({"role": "system", "content": SYSTEM_PROMPT})
+    
+    # Add chat history
+    for user_msg, assistant_msg in chat_history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": assistant_msg})
+    
+    # Add current user input
+    messages.append({"role": "user", "content": user_input})
+    
+    # Format for the model
+    formatted_prompt = ""
+    for message in messages:
+        if message["role"] == "system":
+            formatted_prompt += f"<s>[INST] <<SYS>>\n{message['content']}\n<</SYS>>\n\n"
+        elif message["role"] == "user":
+            if formatted_prompt:
+                formatted_prompt += f"{message['content']} [/INST]"
+            else:
+                formatted_prompt += f"<s>[INST] {message['content']} [/INST]"
+        elif message["role"] == "assistant":
+            formatted_prompt += f" {message['content']} </s><s>[INST] "
+    
+    return formatted_prompt
+
+def generate_response(user_input, chat_history):
+    """Generate a response using the model."""
+    prompt = format_prompt(user_input, chat_history)
+    
     try:
-        response = requests.post(url, headers=headers, json=data)
-        response.raise_for_status()  # Raise an exception for HTTP errors
-        output = response.json()
-        # Adjust the key based on the actual API response format
-        return output["generated_text"]
-    except requests.exceptions.RequestException as e:
-        return f"Error: {str(e)}"
-
-# Set up the Gradio interface
-iface = gr.Interface(
-    fn=generate_response,
-    inputs=gr.Textbox(label="Input your query"),
-    outputs=gr.Textbox(label="Response"),
-    title="Llama 3.1 Nemotron Ultra 253B v1",
-    description="Ask a question and get a response from the AI model."
-)
+        response = pipe(prompt)[0]['generated_text']
+        # Extract only the new content (the model's response)
+        response = response.split('[/INST]')[-1].strip()
+        if '</s>' in response:
+            response = response.split('</s>')[0].strip()
+        
+        # Clean up any remaining tags
+        response = response.replace('<s>', '').replace('</s>', '').replace('[INST]', '').replace('[/INST]', '')
+        
+        return response
+    except Exception as e:
+        return f"I apologize, but I encountered an error: {str(e)}. Please try again with a different query."
+
+def spectral_satya_chat(user_input, chat_history):
+    """Main chat function for the Gradio interface."""
+    response = generate_response(user_input, chat_history)
+    chat_history.append((user_input, response))
+    return "", chat_history
+
+def generate_cinematic_prompt(scene_description, subject_details, camera_preferences, lighting_mood, platform):
+    """Generate a complete cinematic prompt based on user inputs."""
+    prompt = f"""
+    Please create a detailed cinematic prompt for the following scene:
+    
+    Scene Description: {scene_description}
+    Subject Details: {subject_details}
+    Camera Preferences: {camera_preferences}
+    Lighting and Mood: {lighting_mood}
+    Target Platform: {platform}
+    
+    I need both a positive prompt and a negative prompt that follows the principles from The Prompt Engineer's Codex.
+    """
+    
+    chat_history = []
+    response = generate_response(prompt, chat_history)
+    
+    return response
+
+def show_examples():
+    """Return example prompts from the configuration."""
+    return prompt_examples
+
+# Define the Gradio interface
+with gr.Blocks(title="Spectral Satya - Cinematic Prompt Engineer") as demo:
+    gr.Markdown("# 🎬 Spectral Satya - Cinematic Prompt Engineer")
+    gr.Markdown("### Your specialized AI assistant for crafting cinematic reality through expert prompt engineering")
+    
+    with gr.Tab("Chat Interface"):
+        chatbot = gr.Chatbot(height=500)
+        msg = gr.Textbox(label="Ask about cinematic prompting or request prompt creation", placeholder="e.g., 'Create a cinematic prompt for a detective in a rainy alleyway'")
+        clear = gr.Button("Clear")
+        
+        msg.submit(spectral_satya_chat, [msg, chatbot], [msg, chatbot])
+        clear.click(lambda: None, None, chatbot, queue=False)
+    
+    with gr.Tab("Prompt Generator"):
+        with gr.Row():
+            with gr.Column():
+                scene_description = gr.Textbox(label="Scene Description", placeholder="Describe the overall scene you want to create", lines=3)
+                subject_details = gr.Textbox(label="Subject Details", placeholder="Describe the main character/subject in detail", lines=2)
+                camera_preferences = gr.Textbox(label="Camera Preferences", placeholder="Any specific shot types, angles, or movements", lines=2)
+                lighting_mood = gr.Textbox(label="Lighting and Mood", placeholder="Describe the lighting conditions and emotional tone", lines=2)
+                platform = gr.Dropdown(
+                    label="Target Platform", 
+                    choices=["RunwayML", "Pika", "Kling", "Haiper", "Vidu", "Veo", "PixVerse", "Any/Universal"],
+                    value="Any/Universal"
+                )
+                generate_btn = gr.Button("Generate Cinematic Prompt")
+            
+            with gr.Column():
+                output = gr.Textbox(label="Generated Prompt", lines=15)
+        
+        generate_btn.click(
+            generate_cinematic_prompt, 
+            [scene_description, subject_details, camera_preferences, lighting_mood, platform], 
+            output
+        )
+    
+    with gr.Tab("Example Prompts"):
+        examples_output = gr.Markdown()
+        show_examples_btn = gr.Button("Show Example Prompts")
+        show_examples_btn.click(show_examples, None, examples_output)
+    
+    with gr.Tab("About"):
+        gr.Markdown("""
+        ## About Spectral Satya
+        
+        Spectral Satya is a specialized AI assistant focused on crafting cinematic reality through expert prompt engineering for AI video generation. Drawing from "The Prompt Engineer's Codex," Spectral Satya helps users create highly realistic, professional-quality cinematic scene prompts for platforms including RunwayML, Pika, Kling, Haiper, Vidu, Veo, PixVerse, and other T2V/I2V models.
+        
+        ### Core Principles
+        
+        - **Realism Above All**: Always prioritizes photorealistic, cinematic quality
+        - **Specificity is King**: Eliminates vagueness in all prompts
+        - **Show, Don't Tell**: Uses visual language that paints clear pictures
+        - **Defensive Prompting**: Includes robust negative prompts to ward off unwanted styles
+        
+        ### How to Use
+        
+        1. **Chat Interface**: Ask questions about cinematic prompting or request specific prompts
+        2. **Prompt Generator**: Fill in the form fields to generate structured cinematic prompts
+        3. **Example Prompts**: Browse example prompts across different scenarios
+        
+        Created by Hardik Kumawat
+        """)
 
 # Launch the app
-if __name__ == "__main__":
-    iface.launch()
+demo.launch()