1st update app.py

app.py

@@ -1,3 +1,68 @@
 import gradio as gr
 
-gr.load("models/HuggingFaceH4/zephyr-7b-alpha").launch()
+gr.load("models/HuggingFaceH4/zephyr-7b-alpha").launch()
+
+import os
+import numpy as np
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import faiss
+
+# Step 1: Load Precomputed Embeddings and Metadata
+def load_embeddings(embeddings_folder='embeddings'):
+    embeddings = []
+    metadata = []
+    for file in os.listdir(embeddings_folder):
+        if file.endswith('.npy'):
+            embedding = np.load(os.path.join(embeddings_folder, file))
+            embeddings.append(embedding)
+            # Assuming metadata is stored in a corresponding .txt file
+            meta_file = file.replace('.npy', '.txt')
+            with open(os.path.join(embeddings_folder, meta_file), 'r') as f:
+                metadata.append(f.read().strip())
+    return np.array(embeddings), metadata
+
+embeddings, metadata = load_embeddings()
+
+# Step 2: Set Up FAISS Index
+dimension = embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(embeddings)
+
+# Step 3: Load the Language Model
+model_name = "HuggingFaceH4/zephyr-7b-alpha"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Step 4: Define the Retrieval Function
+def retrieve_documents(query, top_k=3):
+    query_embedding = np.mean([embeddings[i] for i in range(len(metadata)) if query.lower() in metadata[i].lower()], axis=0)
+    distances, indices = index.search(np.array([query_embedding]), top_k)
+    retrieved_docs = [metadata[idx] for idx in indices[0]]
+    return retrieved_docs
+
+# Step 5: Define the Response Generation Function
+def generate_response(query):
+    retrieved_docs = retrieve_documents(query)
+    context = " ".join(retrieved_docs)
+    input_text = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
+    inputs = tokenizer(input_text, return_tensors="pt")
+    output = model.generate(**inputs, max_length=512)
+    answer = tokenizer.decode(output[0], skip_special_tokens=True)
+    return answer
+
+# Step 6: Create Gradio Interface
+def gradio_interface(query):
+    response = generate_response(query)
+    return response
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your query here..."),
+    outputs="text",
+    title="RAG-based Course Search",
+    description="Enter a query to search for relevant courses using Retrieval Augmented Generation."
+)
+
+if __name__ == "__main__":
+    iface.launch()