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app.py

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+import re
+import numpy as np
+import faiss
+import torch
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import gradio as gr
+
+
+# --- Helper Functions ---
+def preprocess_text(text):
+    """Clean and preprocess the text by removing multiple newlines and extra spaces."""
+    text = re.sub(r'\n+', '\n', text)
+    text = re.sub(r'[ ]{2,}', ' ', text)
+    return text.strip()
+
+
+def chunk_text(text, max_chunk_size=500, overlap=100):
+    """Chunk the text into smaller parts with overlap."""
+    chunks = []
+    start = 0
+    while start < len(text):
+        end = start + max_chunk_size
+        chunk = text[start:end]
+        chunks.append(chunk)
+        start += max_chunk_size - overlap
+    return chunks
+
+
+def retrieve_relevant_chunks(query, k=3, return_score=False):
+    """Retrieve the most relevant chunks from the script based on the query."""
+    query_embedding = embedding_model.encode([query])
+    distances, indices = index.search(np.array(query_embedding), k)
+    retrieved_chunks = [chunk_lookup[i] for i in indices[0]]
+    similarity_scores = [1 / (1 + d) for d in distances[0]]  # Convert distance to similarity (closer to 1 is better)
+    context = "\n".join(retrieved_chunks)
+    top_score = similarity_scores[0]  # Best match
+    return (context, top_score) if return_score else context
+
+
+def build_prompt(query, context):
+    """Build a prompt for Falcon-7B model with context."""
+    return f"""You are a helpful assistant that answers questions based only on the movie script context provided below.
+
+Context:
+{context}
+
+Question: {query}
+
+Do not answer using your own knowledge. Only use the context. If unsure or if the answer is not in the context, reply: "I cannot answer that as the information is not in the script"
+Answer:"""
+
+
+# --- Load and Preprocess Data ---
+with open("LOTR_script.txt", "r", encoding="utf-8") as file:
+    movie_script = file.read()
+
+movie_script = preprocess_text(movie_script)
+chunks = chunk_text(movie_script)
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+embeddings = embedding_model.encode(chunks, show_progress_bar=True)
+
+dimension = embeddings.shape[1]  # e.g., 384 for MiniLM
+index = faiss.IndexFlatL2(dimension)
+index.add(np.array(embeddings))
+chunk_lookup = {i: chunk for i, chunk in enumerate(chunks)}
+
+# --- Load Falcon Model ---
+model_name = "tiiuae/falcon-7b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+)
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device_map="auto"
+)
+
+
+# --- Main Answering Function ---
+def answer_question(query):
+    """Generate an answer to the query based on relevant chunks."""
+    context, avg_score = retrieve_relevant_chunks(query, k=3, return_score=True)
+    context_str = context[:1500]  # Truncate for model input
+    threshold = 0.4
+    if avg_score < threshold:
+        return f"I don't know.\n\n📊 Avg Similarity Score: {round(avg_score, 2)} (Below threshold)"
+
+    prompt = build_prompt(query, context_str)
+    response = generator(prompt, max_new_tokens=200, do_sample=True, temperature=0.7)[0]["generated_text"]
+
+    if "Answer:" in response:
+        answer = response.split("Answer:")[-1].strip()
+    else:
+        answer = response.strip()
+
+    return f"{answer}\n\n📊 Avg Similarity Score: {round(avg_score, 2)}"
+
+
+# Predefined questions for dropdown menu
+predefined_questions = [
+    "What is the main goal of the Fellowship?",
+    "What is the relationship between Gandalf and Saruman?",
+    "How do the hobbits react when they first see the world outside the Shire?",
+    "What does the city of Isengard represent in Saruman’s betrayal?"
+]
+
+# --- Gradio Interface ---
+interface = gr.Interface(
+    fn=answer_question,
+    inputs=[
+        gr.Dropdown(choices=predefined_questions, label="Select a predefined question"),
+        gr.Textbox(lines=2, placeholder="Or enter your own question..."),
+    ],
+    outputs="text",
+    title="🧝 LOTR Sage (Movie Q&A Bot)",
+    description="Ask questions about The Lord of the Rings (Fellowship of the Ring) movie script. Powered by FAISS + Falcon-7B."
+)
+
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+gradio==3.19.0
+sentence-transformers==2.2.0
+faiss-cpu==1.7.4
+torch==1.13.0
+transformers==4.26.1
+accelerate==0.16.0