diff --git a/data.pth b/data.pth
new file mode 100644
index 0000000..ff50464
Binary files /dev/null and b/data.pth differ
diff --git a/requirements.txt b/requirements.txt
index b279a05..ea3e451 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,12 +1,16 @@
+einops==0.7.0
 fastapi==0.108.0
 google_search_results==2.4.2
 huggingface-hub==0.20.1
 langchain==0.0.352
 nltk==3.8.1
 openai==1.6.1
+Pillow==10.1.0
 psycopg2-binary==2.9.9
 python-dotenv==1.0.0
 python-jose==3.3.0
+python-multipart==0.0.6
 torch==2.1.2
+transformers==4.36.2
 uvicorn==0.25.0
 wikipedia==1.4.0
\ No newline at end of file
diff --git a/src/api/endpoints/story_teller.py b/src/api/endpoints/story_teller.py
new file mode 100644
index 0000000..bb0ef9d
--- /dev/null
+++ b/src/api/endpoints/story_teller.py
@@ -0,0 +1,39 @@
+import io
+
+import torch
+from fastapi import APIRouter, UploadFile, status
+from PIL import Image
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BlipForConditionalGeneration,
+    BlipProcessor,
+)
+
+from api.validators.story import Story
+
+router = APIRouter(prefix="/story-teller", tags=["chatbot"])
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+img_model = BlipForConditionalGeneration.from_pretrained(
+    "Salesforce/blip-image-captioning-large"
+).to(device)
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/phi-2", torch_dtype=torch.float32, trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
+
+
+@router.post("/", status_code=status.HTTP_200_OK, response_model=Story)
+async def Chat(file: UploadFile):
+    image_content = await file.read()
+    raw_image = Image.open(io.BytesIO(image_content)).convert("RGB")
+    inputs = processor(raw_image, return_tensors="pt").to(device)
+    out = img_model.generate(**inputs, max_length=50).to(device)
+    val = processor.decode(out[0], skip_special_tokens=True)
+    text = f"Write a story on the following scenerio:-> {val}"
+    inputs = tokenizer(text, return_tensors="pt", return_attention_mask=False)
+    outputs = model.generate(**inputs, max_length=200).to(device)
+    text = tokenizer.batch_decode(outputs)
+    return {"text": text[0]}
diff --git a/src/api/validators/story.py b/src/api/validators/story.py
new file mode 100644
index 0000000..c59baee
--- /dev/null
+++ b/src/api/validators/story.py
@@ -0,0 +1,5 @@
+from pydantic import BaseModel
+
+
+class Story(BaseModel):
+    text: str
diff --git a/src/main.py b/src/main.py
index 9fc3676..58667ae 100644
--- a/src/main.py
+++ b/src/main.py
@@ -7,6 +7,7 @@
 )
 from api.endpoints.chat import router as chat_router
 from api.endpoints.data_loader import router as data_loader_router
+from api.endpoints.story_teller import router as story_teller_router
 from api.models import response as response_model
 from api.models import user as user_model
 from core.database import engine
@@ -27,3 +28,4 @@
 app.include_router(business_idea_generator_router)
 app.include_router(chat_router)
 app.include_router(data_loader_router)
+app.include_router(story_teller_router)