model name and new api added

app.py

@@ -2,17 +2,28 @@ from fastapi import FastAPI, HTTPException,UploadFile,File
 from pydantic import BaseModel
 from deep_translator import GoogleTranslator
 from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
 import os
 from main import process,audio_process
 from dotenv import load_dotenv
+import base64
+from pathlib import Path
 load_dotenv()
 # Create the FastAPI app instance
 os.makedirs("/tmp/huggingface_cache", exist_ok=True)
 os.environ["HF_HOME"] = "/tmp/huggingface_cache"
 
-
 app = FastAPI()
 
+# Allow all CORS (for local testing)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 # Root endpoint
 @app.get("/")
 async def home():
@@ -52,3 +63,34 @@ async def audio_chat(audio: UploadFile = File(...), token: str = ""):
         return {"result": result}
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+
+UPLOAD_DIR = "uploads"
+Path(UPLOAD_DIR).mkdir(exist_ok=True)
+
+# Request model
+class FileUploadRequest(BaseModel):
+    filename: str
+    content_type: str
+    base64_file: str
+
+@app.post("/summarizer")
+async def upload_base64(file_data: FileUploadRequest):
+    try:
+        print(file_data.filename)
+        file_path = os.path.join(UPLOAD_DIR, file_data.filename)
+
+        # Decode and save file
+        with open(file_path, "wb") as f:
+            f.write(base64.b64decode(file_data.base64_file))
+
+        # Simulate processing
+        extracted_text = f"Saved file: {file_path}\nContent-Type: {file_data.content_type}\n"
+        extracted_text += f"(First 100 bytes shown)\n\n"
+        with open(file_path, "rb") as f:
+            extracted_text += repr(f.read(100))
+
+        return {"text": extracted_text}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

utils/rag.py

@@ -9,7 +9,7 @@ load_dotenv()
 class RAG:
     def __init__(self):
         self.url = 'https://lalitmahale.github.io'
-        self.llm = GoogleGenerativeAI(google_api_key=os.getenv("GOOGLE_API"),model="gemini-pro")
+        self.llm = GoogleGenerativeAI(google_api_key=os.getenv("GOOGLE_API"),model="gemini-1.5-pro")
 
     
     def get_data(self):