Spaces:
Running
Running
poemsforaphrodite
commited on
Commit
•
10ba055
1
Parent(s):
05e591f
Update runner.py
Browse files
runner.py
CHANGED
@@ -1,6 +1,11 @@
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import os
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from openai import OpenAI
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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@@ -53,10 +58,110 @@ def run_custom_model(model_name, question):
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# You'll need to implement this based on how your custom models work
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return f"Custom model {model_name} response: This is a placeholder answer for the question provided."
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-
def
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if model_name == "gpt-4o-mini":
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return run_gpt4o_mini(
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elif model_name == "gpt-4o":
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return run_gpt4o(
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else:
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return run_custom_model(model_name,
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import os
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from openai import OpenAI
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from dotenv import load_dotenv
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import openai
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import io
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from PIL import Image
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import base64 # {{ edit_add: Import base64 for image conversion }}
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import requests # Add this import for making HTTP requests to Hugging Face
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# Load environment variables
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load_dotenv()
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# You'll need to implement this based on how your custom models work
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return f"Custom model {model_name} response: This is a placeholder answer for the question provided."
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def run_huggingface_model(endpoint, token, prompt, context):
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"""
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Runs the Hugging Face model with the provided prompt and context.
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Args:
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endpoint (str): The Hugging Face model endpoint URL.
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token (str): The Hugging Face API token.
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prompt (str): The user's prompt.
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context (str): The context related to the prompt.
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Returns:
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str: The generated response from the Hugging Face model.
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"""
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import os
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import requests
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import json
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headers = {"Authorization": f"Bearer {token}"}
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combined_input = f"{context}\n\n{prompt}" if context else prompt
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payload = {"inputs": combined_input}
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try:
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response = requests.post(endpoint, headers=headers, json=payload)
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response.raise_for_status()
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generated_text = response.json()[0]['generated_text']
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return generated_text
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except requests.exceptions.RequestException as e:
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print(f"Error calling Hugging Face API: {e}")
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return None
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def run_model(model_name, prompt, context=""):
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"""
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Runs the specified model with the given prompt and context.
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Args:
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model_name (str): The name of the model to run.
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prompt (str): The user's prompt.
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context (str, optional): The context related to the prompt. Defaults to "".
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Returns:
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str: The generated response from the model.
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"""
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from pymongo import MongoClient
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from dotenv import load_dotenv
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import os
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# Load environment variables
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load_dotenv()
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# MongoDB connection
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mongodb_uri = os.getenv('MONGODB_URI')
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mongo_client = MongoClient(mongodb_uri)
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db = mongo_client['llm_evaluation_system']
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users_collection = db['users']
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if model_name == "gpt-4o-mini":
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return run_gpt4o_mini(prompt)
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elif model_name == "gpt-4o":
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return run_gpt4o(prompt)
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elif model_name.startswith("HF_"):
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# Fetch model details from the database
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user = users_collection.find_one({"models.model_name": model_name})
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if user:
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model = next((m for m in user['models'] if m['model_name'] == model_name), None)
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if model:
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return run_huggingface_model(model['model_link'], model['model_api_token'], prompt, context)
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print(f"Hugging Face model {model_name} not found")
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return None
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else:
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return run_custom_model(model_name, prompt)
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# {{ edit_final: Add function to summarize images }}
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def summarize_image(image_bytes: bytes) -> str:
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try:
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# Convert bytes to base64
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base64_image = base64.b64encode(image_bytes).decode('utf-8')
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payload = {
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"model": "gpt-4o-mini",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "Please describe and summarize this image."
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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]
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}
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],
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"max_tokens": 300
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}
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response = openai_client.chat.completions.create(**payload)
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summary = response.choices[0].message.content.strip()
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return summary
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except Exception as e:
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print(f"Error in summarize_image: {e}")
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return "Failed to summarize the image."
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