Create app_Aiden20240322.py

app_Aiden20240322.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModel, pipeline as transformers_pipeline, AutoModelForCausalLM
+from diffusers import DiffusionPipeline
+import requests
+from PIL import Image
+import io
+import torch
+import torch.nn.functional as F
+import pandas as pd
+
+# Function for mean pooling of embeddings
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    return sum_embeddings / sum_mask
+
+# Load model and tokenizer from HuggingFace Hub for sentence embeddings
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+def load_image(input_type, uploaded_file=None, image_url=""):
+    """
+    Loads an image from an uploaded file or URL.
+    """
+    if input_type == "Upload Image" and uploaded_file is not None:
+        return Image.open(io.BytesIO(uploaded_file.getvalue()))
+    elif input_type == "Image URL" and image_url:
+        try:
+            response = requests.get(image_url)
+            return Image.open(io.BytesIO(response.content))
+        except Exception as e:
+            st.error(f"Error loading image from URL: {e}")
+            return None
+
+def image_to_caption(image, input_type, uploaded_file, image_url):
+    """
+    Generates a caption for the given image.
+    """
+    image_to_text_pipeline = transformers_pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+    if input_type == "Upload Image" and uploaded_file:
+        return image_to_text_pipeline(uploaded_file.getvalue())[0]['generated_text']
+    elif input_type == "Image URL" and image_url:
+        return image_to_text_pipeline(image_url)[0]['generated_text']
+    return ""
+
+def select_closest_sentence(generated_text):
+    """
+    Selects the sentence closest in meaning to the generated_text.
+    """
+    # Load CSV data
+    df = pd.read_csv('toys_and_games_reviews.csv', encoding='ISO-8859-1')
+    sentences = df.iloc[:, -1].tolist()  # Assuming the last column contains sentences
+
+    # Tokenize and compute embeddings for sentences from CSV
+    encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+    with torch.no_grad():
+        model_output = model(**encoded_input)
+    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+    
+    # Tokenize and compute embedding for the generated_text
+    encoded_new_sentence = tokenizer([generated_text], padding=True, truncation=True, return_tensors='pt')
+    with torch.no_grad():
+        model_output_new_sentence = model(**encoded_new_sentence)
+    new_sentence_embedding = mean_pooling(model_output_new_sentence, encoded_new_sentence['attention_mask'])
+    new_sentence_embedding = F.normalize(new_sentence_embedding, p=2, dim=1)
+
+    # Find the most similar sentence in your corpus
+    most_similar_idx = F.cosine_similarity(new_sentence_embedding, sentence_embeddings).topk(1).indices.item()
+    most_similar_sentence = sentences[most_similar_idx]
+
+    return most_similar_sentence
+
+def generate_text_from_caption(caption):
+    """
+    Generates text based on the provided caption.
+    """
+    text_generator = transformers_pipeline('text-generation', model='pranavpsv/genre-story-generator-v2')
+    generated = text_generator(caption, max_length=100, num_return_sequences=1)
+    return generated[0]['generated_text']
+
+def main():
+    st.title('Image to Story to Image Converter')
+
+    # User interface for input selection
+    input_type = st.radio("Select input type:", ("Upload Image", "Image URL"))
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) if input_type == "Upload Image" else None
+    image_url = st.text_input("Enter the image URL