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app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+import faiss
+from transformers import pipeline
+import numpy as np
+import os
+
+# File paths
+INDEX_FILE = 'ammons_muse_index.faiss'
+EMBEDDINGS_FILE = 'ammons_muse_embeddings.npy'
+CHUNKS_FILE = 'ammons_muse_chunks.npy'
+TEXT_FILE = 'ammons_muse.txt'
+
+# Load and prepare the text
+def prepare_text():
+    with open(TEXT_FILE, 'r', encoding='utf-8') as file:
+        text = file.read()
+    chunk_size = 1000
+    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+
+# Create or load embeddings and index
+def get_embeddings_and_index(chunks):
+    if os.path.exists(INDEX_FILE) and os.path.exists(EMBEDDINGS_FILE):
+        print("Loading existing index and embeddings...")
+        index = faiss.read_index(INDEX_FILE)
+        embeddings = np.load(EMBEDDINGS_FILE)
+    else:
+        print("Creating new index and embeddings...")
+        model = SentenceTransformer('all-MiniLM-L6-v2')
+        embeddings = model.encode(chunks)
+        dimension = embeddings.shape[1]
+        index = faiss.IndexFlatL2(dimension)
+        index.add(embeddings.astype('float32'))
+        
+        # Save index and embeddings
+        faiss.write_index(index, INDEX_FILE)
+        np.save(EMBEDDINGS_FILE, embeddings)
+    
+    return embeddings, index
+
+# Load or create chunks
+if os.path.exists(CHUNKS_FILE):
+    chunks = np.load(CHUNKS_FILE, allow_pickle=True).tolist()
+else:
+    chunks = prepare_text()
+    np.save(CHUNKS_FILE, np.array(chunks, dtype=object))
+
+# Get embeddings and index
+embeddings, index = get_embeddings_and_index(chunks)
+
+# Set up text generation pipeline
+generator = pipeline('text-generation', model='gpt2')
+
+# Retrieval function
+def retrieve_relevant_chunks(query, top_k=3):
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    query_vector = model.encode([query])
+    _, indices = index.search(query_vector.astype('float32'), top_k)
+    return [chunks[i] for i in indices[0]]
+
+# Character response generation
+def generate_character_response(query):
+    relevant_chunks = retrieve_relevant_chunks(query)
+    prompt = f"""As the Muse from A.R. Ammons' poetry, respond to this query:
+Context: {' '.join(relevant_chunks)}
+User: {query}
+Muse:"""
+
+    response = generator(prompt, max_length=150, num_return_sequences=1)[0]['generated_text']
+    return response.split('Muse:')[-1].strip()
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_character_response,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
+    outputs="text",
+    title="A.R. Ammons' Muse Chatbot",
+    description="Ask a question and get a response from the Muse of A.R. Ammons' poetry."
+)
+
+iface.launch()