Update app.py

app.py

@@ -1,21 +1,20 @@
 import os
 import logging
 from fastapi import FastAPI, Request
-from huggingface_hub import InferenceClient, login
+import subprocess
+from huggingface_hub import InferenceClient, login, configure_http_backend
 import langid
 
+# Configure the HTTP backend to use "requests"
+configure_http_backend("requests")  # Set the HTTP backend to "requests"
 
 # Configure logging
-logging.basicConfig(
-    format="%(asctime)s - %(levelname)s - %(message)s",
-    level=logging.INFO
-)
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s", level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
 # Get Hugging Face API token from environment variable
 HF_HUB_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-
 if not HF_HUB_TOKEN:
     raise ValueError("Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN.")
 
@@ -29,7 +28,7 @@ client = InferenceClient(api_key=HF_HUB_TOKEN)
 app = FastAPI()
 
 
-def detect_language(user_input: str) -> str:
+def detect_language(user_input):
     """
     Detect the language of the input text.
     Returns "hebrew" if Hebrew, "english" if English, or "unsupported" otherwise.
@@ -47,7 +46,7 @@ def detect_language(user_input: str) -> str:
         return "unsupported"
 
 
-def generate_response(text: str) -> str:
+def generate_response(text):
     """
     Generate a response based on the input text.
     Selects a prompt and model according to the detected language,
@@ -57,7 +56,7 @@ def generate_response(text: str) -> str:
     if language == "hebrew":
         # Hebrew prompt: answer shortly but explain your decision-making process
         content = "תשמור על תשובה קצרה, אבל תספר איך קיבלת את ההחלטה, " + text
-        model = "mistralai/Mistral-Nemo-Instruct-2407"  # You can change this model as needed.
+        model = "mistralai/Mistral-Nemo-Instruct-2407" 
     elif language == "english":
         content = "keep it short but tell your decision making process, " + text
         model = "mistralai/Mistral-Nemo-Instruct-2407"
@@ -83,8 +82,8 @@ def generate_response(text: str) -> str:
 @app.post("/generate_response")
 async def generate_text(request: Request):
     """
-    API endpoint that accepts a JSON payload with a "text" field,
-    and returns the generated response from the chat model.
+    API endpoint to generate a response from the chat model.
+    Expects a JSON with a "text" field.
     """
     try:
         data = await request.json()
@@ -101,22 +100,23 @@ async def generate_text(request: Request):
 @app.get("/")
 async def root():
     """
-    Root endpoint for checking if the API is running.
+    Root endpoint to check that the API is running.
     """
     return {"message": "Decision Helper API is running!"}
 
 
-# Function to run the Telegram bot
-# def run_bot():
-#     logger.info("Starting Telegram bot...")
-#     # Use subprocess to run bot.py in parallel
-#     import subprocess
-#     subprocess.Popen(["python3", "bot.py"])
+def run_bot():
+    """
+    Start the Telegram bot by running bot.py as a subprocess.
+    """
+    logger.info("Starting Telegram bot...")
+    subprocess.Popen(["python3", "bot.py"])
 
 
 if __name__ == "__main__":
     # When running app.py directly, start the bot as well.
-    # run_bot()
+    run_bot()
     # Uncomment the next lines to run the FastAPI server standalone.
+    # Start the FastAPI server with uvicorn
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)