change

Dockerfile

@@ -13,6 +13,9 @@ RUN pip install --no-cache-dir -r requirements.txt
 # Set environment variable for Hugging Face cache (optional but recommended)
 ENV TRANSFORMERS_CACHE=/tmp/huggingface_cache
 
+# Create the cache directory and make sure it's writable
+RUN mkdir -p /tmp/huggingface_cache && chmod -R 777 /tmp/huggingface_cache
+
 # Expose the port the app will run on (7860 in this case)
 EXPOSE 7860
 

app.py

@@ -4,6 +4,8 @@ from deep_translator import GoogleTranslator
 from fastapi.responses import JSONResponse
 import os
 from main import process,audio_process
+from dotenv import load_dotenv
+load_dotenv()
 # Create the FastAPI app instance
 os.makedirs("/tmp/huggingface_cache", exist_ok=True)
 os.environ["HF_HOME"] = "/tmp/huggingface_cache"
@@ -11,38 +13,46 @@ os.environ["HF_HOME"] = "/tmp/huggingface_cache"
 
 app = FastAPI()
 
-
 # Root endpoint
 @app.get("/")
 async def home():
-    return {"message": "Welcome to my FastAPI API on Hugging Face Spaces!"}
+    return {"message": "Testing_api"}
+
+import os
+from fastapi import FastAPI, HTTPException, UploadFile, File
+from deep_translator import GoogleTranslator
+
+app = FastAPI()
+
+# Token verification function
+def verify_token(token: str):
+    if token != os.getenv("TOKEN"):
+        raise HTTPException(status_code=401, detail="Token not matched")
 
 # Translate endpoint that accepts a query parameter 'text'
 @app.get("/translate")
-async def translate(text: str = ""):
-    if not text:
-        raise HTTPException(status_code=400, detail="No text provided")
-    
-    # Perform translation using deep_translator
+async def translate(text: str = "", token: str = ""):
+    if not text or not token:
+        raise HTTPException(status_code=400, detail="No text or token provided")
+    verify_token(token)
     translator = GoogleTranslator(source="auto", target="mr")
     result = translator.translate(text)
-    
+
     return {"result": result}
 
 @app.get("/chatbot")
-async def chatbot(text: str = ""):
-    if not text:
+async def chatbot(text: str = "", token: str = ""):
+    if not text or not token:
         raise HTTPException(status_code=400, detail="No text provided")
-    # Perform translation using deep_translator
+    verify_token(token)
     result = process(user_query=text)
     return {"result": result}
 
 @app.post("/audio_chat")
-async def audio_chat(audio: UploadFile = File(...)):
-    if not audio:
+async def audio_chat(audio: UploadFile = File(...), token: str = ""):
+    if not audio or not token:
         raise HTTPException(status_code=400, detail="No audio file provided")
-
-    # Example of processing the audio file (you should replace `process` with your actual function)
+    verify_token(token)
     try:
         result = audio_process(audio.file)  # Replace with actual audio processing logic
         return {"result": result}

main.py

@@ -8,20 +8,6 @@ from utils.rag import RAG
 from faster_whisper import WhisperModel
 
 
-# def dump_user_question(query):
-#     try:
-#         if os.path.exists:
-#             with open(r"data\question_data.pkl","rb") as f:
-#                 que = pickle.load(f)
-#         else:
-#             que = []
-#         que.append(query)
-#         with open(r"data\question_data.pkl","wb") as f:
-#                 que = pickle.dump(que,f)
-#     except:
-#         with open(r"data\question_data.pkl","wb") as f:
-#             pickle.dump([],f)
-
 def process(user_query:str):
     # dump_user_question(user_query)
     user_embedding = GetEmbedding([user_query]).user_query_emb()
@@ -46,7 +32,7 @@ def process(user_query:str):
 
 def audio_process(audio):
     try:
-        model = WhisperModel("medium.en")
+        model = WhisperModel(model_size_or_path="medium.en")
         segments, info = model.transcribe(audio)
         transcription = " ".join([seg.text for seg in segments])
         result = process(user_query=transcription)
@@ -62,6 +48,7 @@ if __name__ == "__main__":
     res = audio_process(r"C:\Users\lalit\Documents\Sound recordings\who_is_lalit.m4a")
     print(res)
     # for _ in range(3):
+    
     #     user = input("How can i help you :? \n")
     #     result = process(user)
     #     print(result)