Create app_replit.py

app_replit.py

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+from os import environ
+import logging
+from fastapi import FastAPI, Request
+import subprocess
+# from transformers import pipeline
+from huggingface_hub import InferenceClient, login
+import langid
+# import asyncio
+
+# 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 = environ.get("HUGGINGFACEHUB_API_TOKEN")
+if not HF_HUB_TOKEN:
+    raise ValueError(
+        "Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN.")
+
+# Login and initialize the client
+login(token=HF_HUB_TOKEN)
+client = InferenceClient(api_key=HF_HUB_TOKEN)
+
+# Create FastAPI app
+app = FastAPI()
+
+
+# Function to detect language
+def detect_language(user_input):
+    try:
+        lang, _ = langid.classify(user_input)
+        return "hebrew" if lang == "he" else "english" if lang == "en" else "unsupported"
+    except Exception as e:
+        logging.error(f"Language detection error: {e}")
+        return "unsupported"
+
+
+# Function to generate response
+def generate_response(text):
+    language = detect_language(text)
+
+    if language == "hebrew":
+        content = "תענה בקצרה אבל תשתף את תהליך קבלת ההחלטות שלך, " + text
+        model = "microsoft/Phi-3.5-mini-instruct"
+    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}]
+
+    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
+
+
+@app.post("/generate_response")
+async def generate_text(request: Request):
+    """
+    Endpoint to generate a response from the chat model.
+    Expects a JSON with a "text" field.
+    """
+    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:
+        logging.error(f"Error processing request: {e}")
+        return {"error": "An unexpected error occurred."}
+
+
+@app.get("/")
+async def root():
+    """
+    Root endpoint to check that the API is running.
+    """
+    return {"message": "Decision Helper API is running!"}
+
+
+# Function to run bot.py
+def run_bot():
+    logging.info("Starting Telegram bot...")
+    subprocess.Popen(["python3", "bot.py"])
+
+
+if __name__ == "__main__":
+    run_bot()
+    # import uvicorn
+    # uvicorn.run(app, host="0.0.0.0", port=7860)