Create app.py

app.py

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+import os
+import logging
+from fastapi import FastAPI, Request
+from huggingface_hub import InferenceClient, login
+import langid
+
+
+# Configure logging
+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.")
+
+
+# Login and initialize the InferenceClient
+login(token=HF_HUB_TOKEN)
+client = InferenceClient(api_key=HF_HUB_TOKEN)
+
+
+# Create FastAPI app instance
+app = FastAPI()
+
+
+def detect_language(user_input: str) -> str:
+    """
+    Detect the language of the input text.
+    Returns "hebrew" if Hebrew, "english" if English, or "unsupported" otherwise.
+    """
+    try:
+        lang, _ = langid.classify(user_input)
+        if lang == "he":
+            return "hebrew"
+        elif lang == "en":
+            return "english"
+        else:
+            return "unsupported"
+    except Exception as e:
+        logger.error(f"Language detection error: {e}")
+        return "unsupported"
+
+
+def generate_response(text: str) -> str:
+    """
+    Generate a response based on the input text.
+    Selects a prompt and model according to the detected language,
+    and calls the Hugging Face chat completion API.
+    """
+    language = detect_language(text)
+    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 = "yam-peleg/Hebrew-Gemma-11B-V2"  # You can change this model as needed.
+    elif language == "english":
+        content = "keep it short but tell your decision making process, " + text
+        model = "mistralai/Mistral-Nemo-Instruct-2407"
+    else:
+        return "Sorry, I only support Hebrew and English."
+
+    messages = [{"role": "user", "content": content}]
+
+    try:
+        completion = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            max_tokens=2048,
+            temperature=0.5,
+            top_p=0.7
+        )
+        return completion.choices[0].message.content
+    except Exception as e:
+        logger.error(f"Error generating response: {e}")
+        return "Error: Could not generate response."
+
+
+@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.
+    """
+    try:
+        data = await request.json()
+        text = data.get("text", "").strip()
+        if not text:
+            return {"error": "No text provided"}
+        response = generate_response(text)
+        return {"response": response}
+    except Exception as e:
+        logger.error(f"Error processing request: {e}")
+        return {"error": "An unexpected error occurred."}
+
+
+@app.get("/")
+async def root():
+    """
+    Root endpoint for checking if 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"])
+
+
+if __name__ == "__main__":
+    # When running app.py directly, start the bot as well.
+    run_bot()
+    # Uncomment the next lines to run the FastAPI server standalone.
+    # import uvicorn
+    # uvicorn.run(app, host="0.0.0.0", port=7860)