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app.py

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+import openai
+import os
+from dotenv import load_dotenv, dotenv_values
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from fastapi.responses import RedirectResponse, HTMLResponse
+from fastapi.responses import JSONResponse
+import urllib.parse
+import requests
+
+# Load environment variables (assuming your API key is stored in a `.env` file)
+load_dotenv()
+api_key = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
+
+# OpenAI API configuration (specific to Meta-Llama-3-8B)
+model_link = "meta-llama/Meta-Llama-3-8B-Instruct"
+base_url = "https://router.huggingface.co/v1"
+
+app = FastAPI()
+
+class Message(BaseModel):
+    message: str
+
+@app.get("/", response_class=HTMLResponse)
+async def read_root():
+        return """Welcome to Up to 12 Chat Processor"""
+    
+@app.post("/processtext")
+async def receive_updates(request: Request):
+    data = await request.json()
+    print("Received Update:", data)
+
+    result = process_text(data.get("message", ""))
+    print("Assistant:", result)
+
+    # ضمان إن الرجوع JSON دايمًا
+    return JSONResponse(content=result)
+
+    
+# def process_text(user_text):
+#   """
+#   Processes user text using the Meta-Llama-3-8B model and returns the response.
+#   Args:
+#       user_text: The text entered by the user.
+#   Returns:
+#       The response generated by the model.
+#   """
+
+#   # Initialize OpenAI client
+#   client = openai.OpenAI(api_key=api_key, base_url=base_url)
+
+#   try:
+#     # Generate response using OpenAI chat completion API
+#     response = client.chat.completions.create(
+#         model=model_link,
+#         messages=[{"role": "user", "content": user_text}],
+#         max_tokens=3000,
+#         temperature=0.5,  # Adjust temperature for desired response randomness
+#         stream=False  # Disable streaming for function use
+#     )
+
+#     # Handle potential changes in response format
+#     if isinstance(response.choices, list):
+#       # Access response text if choices is a list (likely scenario)
+#       return response.choices[0].message.content.strip()
+#     else:
+#       # Handle potential alternative response format (less likely)
+#       return response.content.strip() if hasattr(response, 'content') else "An error occurred."
+
+#   except Exception as e:
+#     print(f"Error occurred: {e}")
+#     return "An error occurred while processing your request."
+
+def process_text(user_text: str):
+    client = openai.OpenAI(api_key=api_key, base_url=base_url)
+
+    # 1) تصحيح النص
+    try:
+        system_prompt = f"""
+You are a helpful English grammar corrector for A1/A2 CEFR students.
+Correct grammar, spelling, and literal translation mistakes.
+If the input is only a list of words (not sentences), only correct spelling.
+Return ONLY the revised text (no explanations).
+"""
+
+        resp = client.chat.completions.create(
+            model=model_link,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_text},
+            ],
+            max_tokens=800,
+            temperature=0.1,
+            stream=False,
+        )
+
+        corrected_text = resp.choices[0].message.content.strip()
+        print("After correction:", corrected_text)
+        print("--------------------------------------")
+
+    except Exception as e:
+        # مهم جدًا: رجّع نص error مش object
+        return {"ok": False, "when": "During correcting sentences", "error": str(e)}
+
+    # 2) استخراج الأخطاء (diff)
+    try:
+        mistakes_prompt = f"""
+Compare the user text and the corrected text, and list EVERY correction made.
+Return ONLY a list of mistakes/corrections (no revised text).
+
+User text: "{user_text}"
+Corrected text: "{corrected_text}"
+"""
+
+        resp2 = client.chat.completions.create(
+            model=model_link,
+            messages=[
+                {"role": "system", "content": "You are a grammar checker bot."},
+                {"role": "user", "content": mistakes_prompt},
+            ],
+            max_tokens=800,
+            temperature=0,
+            stream=False,
+        )
+
+        mistakes = resp2.choices[0].message.content.strip()
+
+        return {"ok": True, "Corrected_text": corrected_text, "Mistakes": mistakes}
+
+    except Exception as e:
+        return {"ok": False, "when": "During checking mistakes", "error": str(e), "Corrected_text": corrected_text}

requirements.txt

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+uvicorn
+requests
+fastapi
+numpy<2
+python-multipart
+openai
+langchain
+python-dotenv
+langchain-community