Create app.py

app.py

@@ -0,0 +1,132 @@
+import streamlit as st
+from bertopic import BERTopic
+from PIL import Image
+from transformers import (
+    pipeline,
+    BlenderbotTokenizer,
+    BlenderbotForConditionalGeneration,
+)
+
+# Function to load VQA pipeline
+@st.cache(allow_output_mutation=True)
+def load_vqa_pipeline():
+    return pipeline(task="visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa")
+
+# Function to load BERT-based pipeline
+@st.cache(allow_output_mutation=True)
+def load_bbu_pipeline():
+    return pipeline(task="fill-mask", model="bert-base-uncased")
+
+# Function to load Blenderbot model
+@st.cache(allow_output_mutation=True)
+def load_blenderbot_model():
+    model_name = "facebook/blenderbot-400M-distill"
+    tokenizer = BlenderbotTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)
+    return BlenderbotForConditionalGeneration.from_pretrained(pretrained_model_name_or_path=model_name)
+
+# Function to load GPT-2 pipeline
+@st.cache(allow_output_mutation=True)
+def load_gpt2_pipeline():
+    return pipeline(task="text-generation", model="gpt2")
+
+# Function to load BERTopic models
+@st.cache(allow_output_mutation=True)
+def load_topic_models():
+    topic_model_1 = BERTopic.load(path="davanstrien/chat_topics")
+    topic_model_2 = BERTopic.load(path="MaartenGr/BERTopic_ArXiv")
+    return topic_model_1, topic_model_2
+
+st.title("Georgios Ioannou's Visual Question Answering With Hugging Face")
+st.write("Drag and drop an image file here.")
+
+# Allow the user to upload an image file
+image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if image is not None:
+    # Display the uploaded image
+    image = Image.open(image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+question = st.text_input("What's your question?")
+
+# Load models using the cache
+vqa_pipeline = load_vqa_pipeline()
+bbu_pipeline = load_bbu_pipeline()
+facebook_model = load_blenderbot_model()
+gpt2_pipeline = load_gpt2_pipeline()
+topic_model_1, topic_model_2 = load_topic_models()
+
+# Model 1.
+
+vqa_pipeline_output = vqa_pipeline(image, question, top_k=5)[0]
+
+# Model 2.
+
+text = (
+    "I love "
+    + str(vqa_pipeline_output["answer"])
+    + " and I would like to know how to [MASK]."
+)
+bbu_pipeline_output = bbu_pipeline(text)
+
+# Model 3.
+
+utterance = bbu_pipeline_output[0]["sequence"]
+inputs = tokenizer(utterance, return_tensors="pt")
+result = facebook_model.generate(**inputs)
+facebook_model_output = tokenizer.decode(result[0])
+
+# Model 4.
+
+facebook_model_output = facebook_model_output.replace("<s> ", "")
+facebook_model_output = facebook_model_output.replace("<s>", "")
+facebook_model_output = facebook_model_output.replace("</s>", "")
+gpt2_pipeline_output = gpt2_pipeline(facebook_model_output)[0]["generated_text"]
+
+# Model 5.
+
+topic, prob = topic_model_1.transform(gpt2_pipeline_output)
+topic_model_1_output = topic_model_1.get_topic_info(topic[0])["Representation"][
+    0
+]
+
+topic, prob = topic_model_2.transform(gpt2_pipeline_output)
+topic_model_2_output = topic_model_2.get_topic_info(topic[0])["Representation"][
+    0
+]
+
+st.write("-" * 150)
+st.write("vqa_pipeline_output =", vqa_pipeline_output)
+st.write("bbu_pipeline_output =", bbu_pipeline_output)
+st.write("facebook_model_output =", facebook_model_output)
+st.write("gpt2_pipeline_output =", gpt2_pipeline_output)
+st.write("topic_model_1_output =", topic_model_1_output)
+st.write("topic_model_2_output =", topic_model_2_output)
+st.write("-" * 150)
+
+st.write("SUMMARY")
+st.subheader("Your Image:")
+st.image(image, caption="Your Image", use_column_width=True)
+st.subheader("Your Question:")
+st.write(question)
+st.write("-" * 100)
+
+st.subheader("1. Highest Predicted Answer For Your Question:")
+st.write(vqa_pipeline_output["answer"])
+st.write(text)
+
+st.subheader("2. Highest Predicted Sequence On [MASK] Based on 1.:")
+st.write(bbu_pipeline_output[0]["sequence"])
+
+st.subheader("3. Conversation Based On Previous Answer Based on 2.:")
+st.write(facebook_model_output)
+
+st.subheader("4. Text Generated Based On Previous Answer Based on 3.:")
+st.write(gpt2_pipeline_output)
+
+st.subheader("5. Highest Predicted Topic Model_1 For Previous The Answer Based on 4.:")
+st.write(topic_model_1_output)
+
+st.subheader("6. Highest Predicted Topic Model_2 For Previous The Answer Based on 4.:")
+st.write(topic_model_2_output)
+st.write("-" * 150)