app.py

import gradio as gr
import requests
import json

# Function to get available models from Pollinations API
def get_available_models():
    try:
        response = requests.get("https://text.pollinations.ai/models")
        if response.status_code == 200:
            models_data = response.json()
            # Extract just the model names if API returns complex structure
            if isinstance(models_data, list):
                # If it's a list of strings, return as is
                if all(isinstance(m, str) for m in models_data):
                    return models_data
                # If it's a list of dicts, extract model names/id only
                elif all(isinstance(m, dict) for m in models_data):
                    model_names = []
                    for m in models_data:
                        # Try to get 'name' or 'id' field, ignore everything else
                        if 'name' in m and isinstance(m['name'], str):
                            model_names.append(m['name'])
                        elif 'id' in m and isinstance(m['id'], str):
                            model_names.append(m['id'])
                    return model_names if model_names else [
                        "openai", "mistral", "mistral-large", 
                        "claude-3.5-sonnet", "llama-3.3-70b", "gemini"
                    ]
            # Fallback to default list
            return [
                "openai",
                "mistral",
                "mistral-large",
                "claude-3.5-sonnet",
                "llama-3.3-70b",
                "gemini"
            ]
        else:
            # Fallback list of models
            return [
                "openai",
                "mistral",
                "mistral-large",
                "claude-3.5-sonnet",
                "llama-3.3-70b",
                "gemini"
            ]
    except:
        return [
            "openai",
            "mistral",
            "mistral-large",
            "claude-3.5-sonnet",
            "llama-3.3-70b",
            "gemini"
        ]

# Function to generate text using Pollinations API
def generate_text(prompt, model, seed, system, temperature, max_tokens, top_p):
    if not prompt:
        return "Please enter a prompt."
    
    try:
        # Prepare the API request using the same format as user's code
        url = "https://text.pollinations.ai/"
        
        # Build the query parameters
        params = {
            "model": model,
            "prompt": prompt,
        }
        
        # Add optional parameters if provided
        if seed:
            params["seed"] = int(seed)
        if system:
            params["system"] = system
        if temperature is not None:
            params["temperature"] = temperature
        if max_tokens:
            params["max_tokens"] = int(max_tokens)
        if top_p is not None:
            params["top_p"] = top_p
        
        # Make the request
        response = requests.get(url, params=params)
        
        if response.status_code == 200:
            result_text = response.text
            
            # Try to parse as JSON for better formatting
            try:
                json_result = json.loads(result_text)
                return f"```json\n{json.dumps(json_result, indent=2)}\n```"
            except:
                # Return as plain text if not JSON
                return result_text
        else:
            return f"Error: API returned status code {response.status_code}\n{response.text}"
    
    except Exception as e:
        return f"Error: {str(e)}"

# Get available models
available_models = get_available_models()

# Create Gradio interface
with gr.Blocks(title="Pollinations Text Generator") as demo:
    gr.Markdown(
        """
        # 🌸 Pollinations Text Generator
        Generate text using various AI models via the Pollinations API.
        Select a model and provide a prompt to get started!
        """
    )
    
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(
                label="Prompt",
                placeholder="Enter your text prompt here...",
                lines=5
            )
            
            model_dropdown = gr.Dropdown(
                choices=available_models,
                label="Model",
                value=available_models[0] if available_models else "openai",
                info="Select the AI model to use for text generation"
            )
            
            with gr.Accordion("Advanced Settings", open=False):
                seed_input = gr.Number(
                    label="Seed (optional)",
                    value=None,
                    precision=0,
                    info="Random seed for reproducible results"
                )
                
                system_input = gr.Textbox(
                    label="System Prompt (optional)",
                    placeholder="Enter system instructions...",
                    lines=2,
                    info="System-level instructions for the model"
                )
                
                temperature_slider = gr.Slider(
                    minimum=0,
                    maximum=2,
                    value=0.7,
                    step=0.1,
                    label="Temperature",
                    info="Controls randomness (higher = more creative)"
                )
                
                max_tokens_slider = gr.Slider(
                    minimum=1,
                    maximum=2048,
                    value=512,
                    step=1,
                    label="Max Tokens",
                    info="Maximum length of the generated text"
                )
                
                top_p_slider = gr.Slider(
                    minimum=0,
                    maximum=1,
                    value=0.9,
                    step=0.05,
                    label="Top P",
                    info="Nucleus sampling parameter"
                )
            
            generate_btn = gr.Button("Generate", variant="primary")
        
        with gr.Column():
            output_display = gr.Markdown(
                value="_Your generated text will appear here..._",
                label="Generated Text"
            )
            
            # Add a readonly textbox for easy copying
            with gr.Accordion("Copy Output (Plain Text)", open=False):
                output_copy = gr.Textbox(
                    label="Copyable Output",
                    lines=15,
                    show_copy_button=True,
                    interactive=False
                )
    
    gr.Markdown(
        """
        ### About
        This Space uses the [Pollinations API](https://github.com/pollinations/pollinations) for text generation.
        The API supports multiple models and is free to use.
        
        **Parameters:**
        - **Model**: Choose from available AI models
        - **Seed**: Set a random seed for reproducible outputs
        - **System**: Provide system-level instructions
        - **Temperature**: Control response creativity (0=deterministic, 2=very creative)
        - **Max Tokens**: Set maximum response length
        - **Top P**: Control diversity via nucleus sampling
        """
    )
    
    # Set up the generate button action
    def generate_and_display(prompt, model, seed, system, temp, max_tok, top_p):
        result = generate_text(prompt, model, seed, system, temp, max_tok, top_p)
        # Return both markdown formatted and plain text versions
        return result, result
    
    generate_btn.click(
        fn=generate_and_display,
        inputs=[
            prompt_input, 
            model_dropdown, 
            seed_input,
            system_input,
            temperature_slider, 
            max_tokens_slider, 
            top_p_slider
        ],
        outputs=[output_display, output_copy]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()