Create app.py

app.py

@@ -0,0 +1,93 @@
+from io import BytesIO
+from dotenv import load_dotenv
+import os
+from utils import google_search,split_text_into_chunks,insert_embeddings_into_pinecone_database,query_vector_database,generate_embedding_for_user_resume,delete_vector_namespace
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+import docx
+import fitz 
+load_dotenv()
+
+CX = os.getenv("SEARCH_ENGINE_ID")
+API_KEY = os.getenv("API_KEY")
+PINECONE_API_KEY=os.getenv("PINECONE_API_KEY")
+GEMINI_API_KEY=os.getenv("GEMINI_API_KEY")
+app = FastAPI()
+
+@app.get("/get/course")
+def get_course(query):
+# Example search query
+    results = google_search(query, API_KEY, CX)
+    content=[]
+   
+    if results:
+        for item in results.get('items', []):
+            title = item.get('title')
+            link = item.get('link')
+            snippet = item.get('snippet')
+            content_structure={}
+            
+            content_structure["Course_Title"]=title
+            content_structure["Course_Link"]=link
+            content_structure["Course_Snippet"]= snippet
+            
+            content.append(content_structure)
+        
+    
+    return JSONResponse(content,status_code=200) 
+            
+
+
+
+
+
+@app.post("/upload")
+async def upload_file(user_id,file: UploadFile = File(...)):
+    content = await file.read()  # Read the file content (this will return bytes)
+    sentences=[]
+
+    # Print file details for debugging
+    print(f"File name: {file.filename}")
+    print(f"File content type: {file.content_type}")
+    print(f"File size: {file.size} bytes")
+    
+    
+    if "pdf" == file.filename.split('.')[1]:
+        pdf_document = fitz.open(stream=BytesIO(content), filetype="pdf")
+        # Print the content of the file (if it's text, you can decode it)
+        extracted_text = ""
+        for page_num in range(pdf_document.page_count):
+            page = pdf_document.load_page(page_num)
+            extracted_text += page.get_text()
+            
+    elif "docx" == file.filename.split('.')[1]:
+        docx_file = BytesIO(content)
+        doc = docx.Document(docx_file)
+        extracted_text = ""
+        for para in doc.paragraphs:
+            extracted_text += para.text + "\n"
+            
+    sentences = split_text_into_chunks(extracted_text,chunk_size=200)
+    docs = generate_embedding_for_user_resume(data=sentences,user_id=file.filename)
+    response= insert_embeddings_into_pinecone_database(doc=docs,api_key=PINECONE_API_KEY,name_space=user_id)
+    
+    return {"filename": file.filename,"response":str(response) }    
+
+
+
+
+@app.get("/ask")
+def ask_ai_about_resume(query,user_id):
+    context = query_vector_database(query=query,api_key=PINECONE_API_KEY,name_space=user_id)
+    from google import genai
+
+    client = genai.Client(api_key=GEMINI_API_KEY)
+    response = client.models.generate_content(
+        model="gemini-2.0-flash", contents=f"""
+        Answer this question using the context provided
+        question: {query}
+        context: {context}
+        """
+    )
+    
+    return response.text