Upload final.py

final.py

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+
+import streamlit as st
+from transformers import BlipProcessor, BlipForQuestionAnswering
+from PIL import Image
+
+# Load the processor and model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
+
+# Initialize session state to store chat history
+if 'history' not in st.session_state:
+    st.session_state.history = []
+
+st.title("Conversational Image Recognition Chatbot")
+
+# Upload image
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    
+    # Store the uploaded image in session state
+    st.session_state.image = image
+    
+    # Chat interface
+    user_input = st.text_input("You: ", key="input")
+
+    if st.button("Send"):
+        if user_input:
+
+            # Process the image and question
+            inputs = processor(st.session_state.image, user_input, return_tensors="pt")
+            output = model.generate(**inputs)
+            answer = processor.decode(output[0], skip_special_tokens=True)
+            
+            # Add user question and model answer to chat history
+            st.session_state.history.append({"You": user_input, "chatbot": answer})
+    
+    # Display the chat history
+    if st.session_state.history:
+        for i, chat in enumerate(st.session_state.history):
+            st.write(f"**You:** {chat['You']}")
+            st.write(f"**chatbot:** {chat['chatbot']}")