app.py

import gradio as gr
import requests
import json
import os

# Retrieve the OpenRouter API Key from the Space secrets
API_KEY = os.getenv("OpenRouter_API_KEY")

# Define available models for selection
MODEL_OPTIONS = [
    "openai/gpt-4o-mini-2024-07-18",
    "openai/chatgpt-4o-latest",
    "anthropic/claude-3.5-sonnet",
    "anthropic/claude-3.5-haiku",
    "google/gemini-pro-1.5",
    "google/gemini-flash-1.5",
    "cohere/command-r-plus",
    "meta-llama/llama-3.1-405b-instruct",
    "nvidia/llama-3.1-nemotron-70b-instruct",
    "qwen/qwen-2.5-7b-instruct",
    "mistralai/mistral-large-2411",
    "microsoft/phi-3-medium-128k-instruct",
    "meta-llama/llama-3.1-405b-instruct:free",
    "nousresearch/hermes-3-llama-3.1-405b:free",
    "mistralai/mistral-7b-instruct:free",
    "microsoft/phi-3-medium-128k-instruct:free",
    "liquid/lfm-40b:free",
    "claude-3.5-haiku",
    "nikunj/claude-3.5-haiku",
    "DeepInfra/claude-3.5-haiku",
    "Amazon Bedrock/claude-3.5-haiku"
]

def generate_text(input_text, selected_model, history):
    if history is None:
        history = []  # Initialize history as a list if it's None

    # Append the user's input to the conversation history
    history.append({"role": "user", "content": input_text})
    
    # Call the API to get the model response
    response = requests.post(
        url="https://openrouter.ai/api/v1/chat/completions",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        data=json.dumps({
            "model": selected_model,  # Use the selected model
            "messages": history,  # Send the entire conversation history
            "top_p": 1,
            "temperature": 1,
            "frequency_penalty": 0,
            "presence_penalty": 0,
            "repetition_penalty": 1,
            "top_k": 0,
            "max_tokens": 8000
        })
    )
    
    # Handle errors
    if response.status_code != 200:
        return f"Error: {response.status_code}, {response.text}", history, history
    
    # Parse and generate the response
    try:
        response_json = response.json()
        generated_response = response_json.get("choices", [{}])[0].get("message", {}).get("content", "No content returned.")
    except json.JSONDecodeError:
        generated_response = "Error: Unable to parse response."

    # Append the model's response to the conversation history
    history.append({"role": "assistant", "content": generated_response})
    
    # Format the history for display
    display_history = "\n\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in history])
    
    return generated_response, display_history, history  # Return the updated history for continuity


# Inject custom CSS directly into the Gradio interface for scrollbars
css = """
    .output-textbox {
        height: 150px;
        overflow: auto;
        border: 1px solid #ddd;
        padding: 10px;
    }
    .history-textbox {
        height: 300px;
        overflow: auto;
        border: 1px solid #ddd;
        padding: 10px;
    }
"""

# Create Gradio interface with a dropdown for model selection
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(lines=2, label="Input Text", placeholder="Enter your query here"),
        gr.Dropdown(choices=MODEL_OPTIONS, label="Select Model", value=MODEL_OPTIONS[0]),
        gr.State()  # This is where we maintain the history state (input)
    ],
    outputs=[
        gr.Textbox(label="Response", placeholder="Response will be shown here", elem_classes=["output-textbox"]),
        gr.Textbox(label="History", placeholder="Interaction history will be shown here", lines=10, interactive=False, elem_classes=["history-textbox"]),
        gr.State()  # History needs to be output as well
    ],
    title="Chat with OpenRouter Models",
    css=css  # Apply the custom CSS here
)

iface.launch()