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app.py

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+import gradio as gr
+from llm_handler import generate_response
+
+# Predefined system prompts
+SYSTEM_PROMPTS = [
+    "You are a professional coding assistant",
+    "You are a professional teaching assistant",
+    "You are professional email writer",
+    "Other"
+]
+
+# Available models
+MODELS = [
+    "Claude Haiku",
+    "DeepSeek",
+    "Claude Premium",
+    "GPT Pro"
+]
+
+
+def handle_system_prompt_change(choice):
+    if choice == "Other":
+        return gr.Textbox(visible=True)
+    return gr.Textbox(visible=False)
+
+
+def process_input(user_prompt, model_choice, system_prompt_choice, custom_system_prompt):
+    # Use custom system prompt if "Other" is selected
+    final_system_prompt = custom_system_prompt if system_prompt_choice == "Other" else system_prompt_choice
+
+    if not user_prompt:
+        return "Please enter a prompt"
+
+    # Generate response using llmhandler
+    response = generate_response(user_prompt, model_choice, final_system_prompt)
+    return response
+
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# LLM Interface")
+
+    with gr.Row():
+        with gr.Column():
+            user_prompt = gr.Textbox(
+                label="Your Prompt",
+                placeholder="Enter your prompt here...",
+                lines=5
+            )
+
+            model_choice = gr.Dropdown(
+                choices=MODELS,
+                label="Select Model",
+                value=MODELS[0]
+            )
+
+            system_prompt_choice = gr.Dropdown(
+                choices=SYSTEM_PROMPTS,
+                label="Select System Prompt",
+                value=SYSTEM_PROMPTS[0]
+            )
+
+            custom_system_prompt = gr.Textbox(
+                label="Custom System Prompt",
+                placeholder="Enter your custom system prompt...",
+                visible=False
+            )
+
+            submit_btn = gr.Button("Generate Response")
+
+        with gr.Column():
+            output = gr.Textbox(
+                label="Response",
+                lines=10,
+                show_copy_button=True  # Adding copy button to the output
+            )
+
+    # Handle system prompt change
+    system_prompt_choice.change(
+        fn=handle_system_prompt_change,
+        inputs=[system_prompt_choice],
+        outputs=[custom_system_prompt]
+    )
+
+    # Handle submit button click
+    submit_btn.click(
+        fn=process_input,
+        inputs=[
+            user_prompt,
+            model_choice,
+            system_prompt_choice,
+            custom_system_prompt
+        ],
+        outputs=[output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

llm_handler.py

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+import os
+from openai import OpenAI
+from dotenv import load_dotenv
+import logging
+
+load_dotenv()
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def generate_response(user_prompt, model_name, system_prompt):
+    try:
+        # Get API key from environment variable
+        api_key = os.getenv("OPENROUTER_API_KEY")
+
+        if not api_key:
+            return "Error: API key not found in environment variables"
+
+        # Initialize OpenRouter client using OpenAI with custom base URL
+        client = OpenAI(
+            base_url="https://openrouter.ai/api/v1",
+            api_key=api_key,
+            default_headers={
+                "HTTP-Referer": "null",
+                "X-Title": "LLMInterface",
+            }
+        )
+
+        # Map display names to actual model names
+        model_mapping = {
+            "Claude Haiku": "anthropic/claude-3.5-haiku-20241022:beta",
+            "DeepSeek": "deepseek/deepseek-r1:free",
+            "Claude Premium": "anthropic/claude-3.5-sonnet",
+            "GPT Pro": "openai/gpt-4-0125-preview"
+        }
+
+        # Get the actual model name
+        actual_model = model_mapping.get(model_name)
+        if not actual_model:
+            return "Error: Invalid model selection"
+
+        # Make the API call
+        response = client.chat.completions.create(
+            model=actual_model,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ]
+        )
+
+        return response.choices[0].message.content
+
+    except Exception as e:
+        logger.error(f"Error in generate_response: {str(e)}")
+        return f"Error occurred: {str(e)}"

requirements.txt

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+streamlit
+openai==1.58.1
+python-dotenv==1.0.0
+gradio>=3.0
+openrouter