Athspi-promax / app.py
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import os
import time
import json
import httpx
import gradio as gr
from huggingface_hub import InferenceClient
from dotenv import load_dotenv
# Load API keys from .env file
load_dotenv()
HF_API_KEY = os.getenv("HF_API_KEY") # Hugging Face API for Gemma & Llama
TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY") # Together AI API for DeepSeek-V3
# Initialize Hugging Face Clients
hf_client = InferenceClient(provider="hf-inference", api_key=HF_API_KEY)
together_client = InferenceClient(provider="together", api_key=TOGETHER_API_KEY)
# Query Hugging Face Models (Gemma & Llama)
def query_huggingface_model(user_input, model_name):
try:
messages = [{"role": "user", "content": user_input}]
completion = hf_client.chat.completions.create(
model=model_name,
messages=messages,
max_tokens=500
)
return completion.choices[0].message["content"]
except Exception as e:
return f"Error querying {model_name}: {str(e)}"
# Query DeepSeek-V3 (Together AI via Hugging Face)
def query_deepseek_v3(user_input):
try:
messages = [{"role": "user", "content": user_input}]
completion = together_client.chat.completions.create(
model="deepseek-ai/DeepSeek-V3",
messages=messages,
max_tokens=500
)
return completion.choices[0].message["content"]
except Exception as e:
return f"Error querying DeepSeek-V3: {str(e)}"
# Function to refine responses using DeepSeek-V3
def refine_response(user_input):
try:
# Get responses from all three models
gemma_response = query_huggingface_model(user_input, "google/gemma-2-27b-it")
llama_response = query_huggingface_model(user_input, "meta-llama/Llama-3.3-70B-Instruct")
deepseek_response = query_deepseek_v3(user_input)
# If any response is missing, return the available ones
responses = {
"Gemma": gemma_response.strip(),
"Llama": llama_response.strip(),
"DeepSeek-V3": deepseek_response.strip()
}
valid_responses = {k: v for k, v in responses.items() if v}
if len(valid_responses) < 2:
return "\n\n".join(f"{k} Response: {v}" for k, v in valid_responses.items())
# Prepare refinement prompt
improvement_prompt = f"""
Here are three AI-generated responses:
Response 1 (Gemma): {gemma_response}
Response 2 (Llama 3.3): {llama_response}
Response 3 (DeepSeek-V3): {deepseek_response}
Please combine the best elements of all three, improve clarity, and provide a final refined answer.
"""
# Retry loop for DeepSeek-V3 refinement
max_retries = 3
for attempt in range(max_retries):
try:
messages = [{"role": "user", "content": improvement_prompt}]
refined_completion = together_client.chat.completions.create(
model="deepseek-ai/DeepSeek-V3",
messages=messages,
max_tokens=500
)
refined_content = refined_completion.choices[0].message["content"]
if refined_content.strip():
return refined_content
else:
print("Received empty response from DeepSeek-V3, retrying...")
time.sleep(2)
except Exception as e:
print(f"Error on attempt {attempt + 1}: {str(e)}")
time.sleep(2)
return f"Refinement failed. Here’s the best available response:\n\n{max(valid_responses.values(), key=len)}"
except Exception as e:
return f"Error refining response: {str(e)}"
# Create Gradio interface
iface = gr.Interface(
fn=refine_response,
inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."),
outputs="text",
title="Multi-Model AI Enhancer",
description="Get responses from Gemma, Llama 3.3, and DeepSeek-V3. Then receive an improved final answer."
)
# Launch app
iface.launch(debug=True)