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import gradio as gr |
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import os |
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from openai import OpenAI |
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") |
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openai_client = OpenAI(api_key=OPENAI_API_KEY) |
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DEEPSEEK_API_KEY = so.getenv("DEEPSEEK_API_KEY") |
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deepseek_client = OpenAI(api_key=DEEPSEEK_API_KEY,base_url="https://api.deepseek.com" |
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def generate_response(prompt, model_provider, temperature, top_p, max_tokens, repetition_penalty): |
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try: |
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response = openai_client.chat.completions.create( |
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model="gpt-3.5-turbo", |
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messages=[{"role": "user", "content": prompt}], |
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temperature=temperature, |
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top_p=top_p, |
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max_tokens=max_tokens, |
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presence_penalty=repetition_penalty, |
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stream=False |
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) |
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return response.choices[0].message.content.strip() |
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except Exception as e: |
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return f"OpenAI API Error: {str(e)}" |
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iface = gr.Interface( |
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fn=generate_response, |
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inputs=[ |
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gr.Textbox(label="Prompt", lines=6, placeholder="Ask something..."), |
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gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p"), |
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gr.Slider(minimum=32, maximum=2048, value=512, step=32, label="Max New Tokens"), |
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gr.Slider(minimum=1.0, maximum=2.0, value=1.1, step=0.1, label="Repetition Penalty") |
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], |
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outputs="text", |
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title="🧠 DeepSeek LLM Chat with Parameter Tuning", |
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theme=gr.themes.Soft() |
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) |
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iface.launch() |
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