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import gradio as gr
from helper import *
from prompts_and_chema import *
def full_chat_pipeline(user_query, openai_key):
try:
# ✅ Step 1: Set the OpenAI API key
openai.api_key = openai_key
# Step 1: Get intent from first GPT call
raw_response = chat_with_gpt(intent_system_prompt, user_query, use_schema=True, schema=intent_output_schema )
# print(f"raw_response: {raw_response}")
intents = json.loads(raw_response) if isinstance(raw_response, str) else raw_response
# print(f"Intents: {intents}")
# Step 2: Format prompt with detected intent
loaded_texts = load_intent_texts(intents, get_txt_files)
#selected_intents = ', '.join(intents.get("intents", []))
selected_intent_description = load_intent_description(intents, intent_description_map)
print(selected_intent_description)
context_str = '\n\n'.join(f"{k}:\n{v}" for k, v in loaded_texts.items())
formatted_prompt = f'''
User query: {user_query}
Selected Intents: {selected_intent_description}
Context: {context_str}
'''
# Step 3: Get final answer from second GPT call
final_response = chat_with_gpt(get_answer_system_prompt, formatted_prompt)
# print("Final Response:", final_response)
return f"{str(intents)}", f"{final_response}"
except Exception as e:
return f"Error while classifying intents: {str(e)}", f"Error while generating response: {str(e)}"
# Gradio UI
with gr.Blocks(title="Degirum LLM") as demo:
gr.Markdown("## Degirum LLM")
gr.Markdown("Ask questions...")
# 🔑 OpenAI Key on top
openai_key = gr.Textbox(label="🔑 OpenAI API Key", placeholder="Enter your OpenAI API key...", type="password")
# Row with query input and intent output side by side
with gr.Row():
user_query = gr.Textbox(label="💬 Query", placeholder="Type your question here...", lines=3)
intent_output = gr.Textbox(label="🧠 Classified Intents", placeholder="will get answer by AI", lines=3, interactive=False)
# 🚀 Submit Button
submit_btn = gr.Button("🚀 Submit", variant="primary")
# 🤖 AI Response at the bottom
response_output = gr.Textbox(label="🤖 Full AI Response", lines=10, interactive=False)
submit_btn.click(
fn=full_chat_pipeline,
inputs=[user_query, openai_key],
outputs=[intent_output, response_output]
)
if __name__ == "__main__":
demo.launch()
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