updated streamlit file

.ipynb_checkpoints/start-checkpoint.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # Start FastAPI
-uvicorn app:app --host 0.0.0.0 --port 8502 &
+# uvicorn app:app --host 0.0.0.0 --port 8502 &
 
 # Start Streamlit
 streamlit run streamlit_app.py \

.ipynb_checkpoints/streamlit_app-checkpoint.py

@@ -6,16 +6,16 @@ import torch
 import io
 
 
-# @st.cache_resource
-# def load_model():
-#     model = Blip2ForConditionalGeneration.from_pretrained("ybelkada/blip2-opt-2.7b-fp16-sharded")
-#     model.load_adapter('blip-cpu-model')
-#     processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
-#     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-#     model.to(device)
-#     return model, processor
+@st.cache_resource
+def load_model():
+    model = Blip2ForConditionalGeneration.from_pretrained("ybelkada/blip2-opt-2.7b-fp16-sharded")
+    model.load_adapter('blip-cpu-model')
+    processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    return model, processor
 
-# model, processor = load_model()
+model, processor = load_model()
 
 st.title("Image Captioning with Fine-Tuned BLiPv2 Model")
 
@@ -25,16 +25,16 @@ if uploaded_file is not None:
     image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image", use_column_width=True)
     
-    files = {"file": uploaded_file.getvalue()}
-    print("Sending API request")
-    response = requests.post("http://0.0.0.0:8502/generate-caption/", files=files)
-    caption = response.json().get("caption")
+    # files = {"file": uploaded_file.getvalue()}
+    # print("Sending API request")
+    # response = requests.post("http://0.0.0.0:8502/generate-caption/", files=files)
+    # caption = response.json().get("caption")
     
-#     inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
+    inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
     
-#     with torch.no_grad():
-#         caption_ids = model.generate(**inputs, max_length=128)
-#         caption = processor.decode(caption_ids[0], skip_special_tokens=True)
+    with torch.no_grad():
+        caption_ids = model.generate(**inputs, max_length=128)
+        caption = processor.decode(caption_ids[0], skip_special_tokens=True)
     
     st.write("Generated Caption:")
     st.write(f"**{caption}**")

start.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # Start FastAPI
-uvicorn app:app --host 0.0.0.0 --port 8502 &
+# uvicorn app:app --host 0.0.0.0 --port 8502 &
 
 # Start Streamlit
 streamlit run streamlit_app.py \

streamlit_app.py

@@ -6,16 +6,16 @@ import torch
 import io
 
 
-# @st.cache_resource
-# def load_model():
-#     model = Blip2ForConditionalGeneration.from_pretrained("ybelkada/blip2-opt-2.7b-fp16-sharded")
-#     model.load_adapter('blip-cpu-model')
-#     processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
-#     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-#     model.to(device)
-#     return model, processor
+@st.cache_resource
+def load_model():
+    model = Blip2ForConditionalGeneration.from_pretrained("ybelkada/blip2-opt-2.7b-fp16-sharded")
+    model.load_adapter('blip-cpu-model')
+    processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    return model, processor
 
-# model, processor = load_model()
+model, processor = load_model()
 
 st.title("Image Captioning with Fine-Tuned BLiPv2 Model")
 
@@ -25,16 +25,16 @@ if uploaded_file is not None:
     image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image", use_column_width=True)
     
-    files = {"file": uploaded_file.getvalue()}
-    print("Sending API request")
-    response = requests.post("http://0.0.0.0:8502/generate-caption/", files=files)
-    caption = response.json().get("caption")
+    # files = {"file": uploaded_file.getvalue()}
+    # print("Sending API request")
+    # response = requests.post("http://0.0.0.0:8502/generate-caption/", files=files)
+    # caption = response.json().get("caption")
     
-#     inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
+    inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
     
-#     with torch.no_grad():
-#         caption_ids = model.generate(**inputs, max_length=128)
-#         caption = processor.decode(caption_ids[0], skip_special_tokens=True)
+    with torch.no_grad():
+        caption_ids = model.generate(**inputs, max_length=128)
+        caption = processor.decode(caption_ids[0], skip_special_tokens=True)
     
     st.write("Generated Caption:")
     st.write(f"**{caption}**")