app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import requests

def generate_answer(question, model_name, temperature):
    prompt = "Answer the following question: " + question
    if model_name == "phi1.5":
        tokenizer = AutoTokenizer.from_pretrained("phi1.5")
        input_ids = tokenizer.encode(prompt, return_tensors="pt")
        output = llm_model.generate(input_ids, max_length=512, do_sample=True, top_k=50, top_p=0.9, temperature=temperature)[0]
        answer = tokenizer.decode(output, skip_special_tokens=True)
        end_of_text_index = answer.find("(end of text)")
        if end_of_text_index > -1:
            answer = answer[:end_of_text_index]
        return answer
    elif model_name == "Google Gemini":
        url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"
        headers = {"Content-Type": "application/json", "X-goog-api-key": "AIzaSyCfiLsxlpQcdG6hGwCft6-yO4K2c-kp6-o"}
        data = {
            "contents": [{"parts": [{"text": prompt}]}],
            "generationConfig": {"temperature": temperature}
        }
        response = requests.post(url, headers=headers, json=data)
        if response.status_code == 200:
            try:
                return response.json()["candidates"][0]["content"]["parts"][0]["text"]
            except Exception:
                return "Error: Unexpected Gemini API response format."
        else:
            return f"Error: Gemini API call failed ({response.status_code})"
    else:
        return "Invalid model selection."

def chatbot(question, model_name, temperature):
    return generate_answer(question, model_name, temperature)

if __name__ == "__main__":
    llm_model = AutoModelForCausalLM.from_pretrained("phi1.5", trust_remote_code=True)
    with gr.Blocks(theme="default") as demo:
        gr.Markdown("# I am your AI Health Assistance 🏥\nAsk general health related questions to the AI Bot.")
        model_name = gr.Dropdown(["phi1.5", "Google Gemini"], value="phi1.5", label="Model Selection")
        temperature = gr.Slider(0.0, 1.0, value=0.7, label="Temperature")
        question = gr.Textbox(lines=2, label="Your Question")
        output = gr.Textbox(lines=10, label="AI Response", interactive=False)
        ## connect to backend
        def run_chatbot(q, m, t):
            return chatbot(q, m, t)
        submit_btn = gr.Button("Submit")
        submit_btn.click(run_chatbot, inputs=[question, model_name, temperature], outputs=output)
    demo.launch()