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import os | |
from huggingface_hub import InferenceClient | |
# Initialize client without provider (Hugging Face handles routing) | |
client = InferenceClient( | |
model="Qwen/Qwen2.5-7B-Instruct", | |
token=os.environ.get("HF_TOKEN") # Make sure HF_TOKEN is set in Secrets | |
) | |
def analyze_data(prompt): | |
""" | |
Use chat completions API to generate insights from raw search data | |
""" | |
try: | |
# Format prompt as a chat message | |
messages = [ | |
{ | |
"role": "user", | |
"content": prompt | |
} | |
] | |
# Get response from LLM | |
completion = client.chat.completions.create( | |
messages=messages, | |
max_tokens=4096 # Control response length | |
) | |
# Return only the content part of the response | |
return completion.choices[0].message.content | |
except Exception as e: | |
return f"LLM generation failed: {str(e)}" |