add snapshot to load the models

.gitignore

@@ -0,0 +1 @@
+models/

main.py

@@ -9,20 +9,59 @@ import io
 import base64
 import matplotlib.pyplot as plt 
 import torch
-import tensorflow
-from transformers import pipeline,VisionEncoderDecoderModel,ViTImageProcessor,AutoTokenizer
-from transformers import BartForConditionalGeneration, BartTokenizer
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import tensorflow as tf
+
 import fitz
 from docx import Document
 from pptx import Presentation
 import seaborn as sns
 import PIL.Image as Image
+import fitz
 
+from huggingface_hub import snapshot_download
+from transformers import (
+    TFAutoModelForVision2Seq, AutoProcessor,
+    AutoTokenizer, AutoModelForSeq2SeqLM,
+    AutoModelForCausalLM,pipeline
+)
 
+# === 1. Load BLIP Image Captioning (TensorFlow) ===
+try: 
+    print("[Info] installing Salesforce/blip-image-captioning-base ....")
+    blip_dir = "./models/blip-base-tf"
+    snapshot_download("Salesforce/blip-image-captioning-base", local_dir=blip_dir, local_dir_use_symlinks=False)
+    interpreter = pipeline("image-captioning", model="Salesforce/blip-image-captioning-base")
+    print("[Info] Salesforce/blip-image-captioning-base is inatalled.")
+except Exception as exp:
+    print("Can't load the model Salesforce/blip-image-captioning-base")
+    print(f"[Error] {str(exp)}")
 
-import fitz
+# === 2. Load BART Summarization (PyTorch) ===
+try:
+    print("[Info] installing facebook/bart-large-cnn ....")
+    bart_dir = "./models/bart-large-cnn"
+    snapshot_download("facebook/bart-large-cnn", local_dir=bart_dir, local_dir_use_symlinks=False)
+    bart_tokenizer = AutoTokenizer.from_pretrained(bart_dir)
+    bart_model = AutoModelForSeq2SeqLM.from_pretrained(bart_dir)
+    summarizer = pipeline("summarization", model=bart_model, tokenizer=bart_tokenizer)
+    print("[Info] facebook/bart-large-cnn is installed")
+except Exception as exp:
+    print("Can't load the model facebook/bart-large-cnn")
+    print(f"[Error] {str(exp)}")
 
+# === 3. Load DeepSeek Coder (PyTorch with trust_remote_code) ===
+try:
+    print("[Info] installing deepseek-ai/deepseek-coder-1.3b-instruct ")
+    deepseek_dir = "./models/deepseek-coder"
+    snapshot_download("deepseek-ai/deepseek-coder-1.3b-instruct", local_dir=deepseek_dir, local_dir_use_symlinks=False)
+    deepseek_tokenizer = AutoTokenizer.from_pretrained(deepseek_dir, trust_remote_code=True)
+    deepseek_model = AutoModelForCausalLM.from_pretrained(deepseek_dir, trust_remote_code=True)
+    generator = pipeline("text-generation", model=deepseek_model, tokenizer=deepseek_tokenizer)
+
+    print("[Info] facebook/bart-large-cnn is installed")
+except Exception as exp:
+    print("Can't load the model deepseek-ai/deepseek-coder-1.3b-instruct")
+    print(f"[Error] {str(exp)}")
 
 
 app=FastAPI()
@@ -34,65 +73,19 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
-try:
-    interpreter =None
-    print("installing interpreter ...")
-    interpreter =pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
-    if interpreter is None :
-        print("\n\n interpreter is nonne \n\n")
-    else:
-        print(" interpreter installed.")
-    #interpreter_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-    #interpreter_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-    #interpreter_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-except Exception as exp:
-    print("[ERROR] Can't load nlpconnect/vit-gpt2-image-captioning")
-    print(str(exp))
 
 
 
-try:
-    summarizer=None
-    print ("installing summarizer ...")
-    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-    if summarizer is None :
-        print("\n\n summarizer is nonne \n\n")
-    else:
-        print(" summarizer installed.")
-except Exception as exp:
-    print("[ERROR] Can't load facebook/bart-large-cnn ")
-    print(str(exp))
 
-#try:
-#    summarizer_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
-#except OSError as e:
-#    print(f"[INFO] PyTorch weights not found. Falling back to TensorFlow weights.\n{e}")
-#    summarizer_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn", from_tf=True)
 
-#summarizer_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
 
 
 
-try:
-    generator=None
-    print("installing generator ...")
-    generator = pipeline("text-generation", model="deepseek-ai/deepseek-coder-1.3b-instruct")
-    if generator is None :
-        print("\n\n generator is nonne \n\n")
-    else:
-        print(" generator installed.")
-except Exception as exp:
-    print("[ERROR] Can't load deepseek-ai/deepseek-coder-1.3b-instruct ")
-    print(str(exp))
 
 
-#try:
-#    generator_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True)
-#    tokengenerator_modelizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True)
-#except Exception as exp :
-#    print("[ERROR] Can't load deepseek-ai/deepseek-coder-1.3b-instruct ")
-#    print(str(exp))
+
+
+
 
 
 app.mount("/static",StaticFiles(directory='static'),'static')
@@ -112,14 +105,6 @@ def index(req:Request):
     return  templates.TemplateResponse('ImageInterpretation.html',{'request':req})
 
 
-@app.post('/get')
-def g(f:str):
-   global generator
-   return generator(f)[0]["generated_text"]
-@app.post('/gets')
-def g(f:str):
-   global summarizer
-   return summarizer(f)[0]['summary_text']
 
 
     
@@ -130,12 +115,10 @@ def caption(file:UploadFile=File(...)):
     if extension not in Supported_extensions:
         return {"error": "Unsupported file type"}
     image = Image.open(file.file)
-     
+    global interpreter
+    
     caption = interpreter(image)
-    #pixel_values = interpreter_processor(images=image, return_tensors="pt").pixel_values
-    #output_ids = interpreter_model.generate(pixel_values, max_length=16, num_beams=4)
-    #caption = interpreter_tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    #return {"caption":caption}
+    
     return {"caption": caption[0]['generated_text']}   
 
 @app.post("/summerize")
@@ -154,8 +137,13 @@ def summerzation(file:UploadFile=File(...)):
         return {"error": "Unsupported file type"}
     
     result=""
-    for i in range(0,len(text),1024):
-        result+=summarizer(text, max_length=150, min_length=30, do_sample=False)[0]['summary_text']
+    global summarizer
+    for i in range(0, len(text), 1024):
+        try:
+            summary = summarizer(text[i:i+1024], max_length=150, min_length=30, do_sample=False)
+            result += summary[0]['summary_text']
+        except Exception as e:
+            return {"error": f"Summarization failed: {str(e)}"}
     return {"summary": result}
 
 
@@ -193,7 +181,7 @@ error.
 
 ##Prompt: {prompt}.
         """
-        
+        global generator
         output = generator(message, max_length=1000)
         match = re.search(r'```python(.*?)```', output[0]["generated_text"], re.DOTALL)
         code =''