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import requests
import json
def send_lm_studio_request(model, prompt_file, output_file):
"""
Sends a request to the LM Studio server with the prompt from a file
and saves the response to another file.
"""
# Update the URL to LM Studio's local server
url = "http://localhost:8000/v1/completions" # Adjust port if necessary
headers = {'Content-Type': 'application/json'}
try:
# Read the prompt from the file
with open(prompt_file, 'r') as f:
prompt = f.read()
except FileNotFoundError:
print(f"Error: Prompt file '{prompt_file}' not found.")
return
# Define the request payload
data = {
"model": model, # Use the qwen2.5-coder-3b model
"prompt": prompt,
"max_tokens": 512, # Optional: Set max tokens for the response
"temperature": 0.7 # Optional: Control randomness of the output
}
try:
# Send the POST request to the LM Studio API
response = requests.post(url, headers=headers, data=json.dumps(data))
response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
except requests.exceptions.RequestException as e:
print(f"Error: Could not connect to LM Studio API: {e}")
return
if response.status_code == 200:
try:
# Parse the JSON response
response_json = response.json()
# Extract the generated text (adjust key based on LM Studio's response structure)
response_text = response_json.get('choices', [{}])[0].get('text', '').strip()
except json.JSONDecodeError:
print("Error: Could not decode JSON response.")
return
try:
# Save the response to the output file
with open(output_file, 'w') as f:
f.write(response_text)
print(f"Response saved to '{output_file}'.")
except IOError as e:
print(f"Error: Could not write to output file: {e}")
else:
print(f"Error: API request failed with status code {response.status_code}")
if __name__ == "__main__":
model_name = "qwen2.5-coder-3b" # Replace with your desired model
prompt_file = "req.txt" # Path to the file containing the prompt
output_file = "resp.txt" # Path to the file where the response will be saved
send_lm_studio_request(model_name, prompt_file, output_file)
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