added fast api again

.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

@@ -6,17 +6,17 @@ 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()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# model, processor = load_model()
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 st.title("Image Captioning with Fine-Tuned BLiPv2 Model")
 
@@ -26,16 +26,16 @@ 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

@@ -6,17 +6,17 @@ 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()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# model, processor = load_model()
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 st.title("Image Captioning with Fine-Tuned BLiPv2 Model")
 
@@ -26,16 +26,16 @@ 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}**")