Create app.py

app.py

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+# app.py
+import numpy as np
+import faiss
+from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
+import gradio as gr
+import torch
+from tqdm import tqdm
+from groq import Groq
+
+# Load dataset
+dataset = load_dataset("midrees2806/7K_Dataset")
+print("Dataset sample:", dataset['train'][0])
+
+# Initialize sentence transformer model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = model.to(device)
+
+# Batch processing function
+def generate_embeddings_batch(texts):
+    return model.encode(texts, batch_size=1024, convert_to_tensor=True, device=device).cpu().numpy()
+
+# Prepare embeddings
+train_dataset = dataset['train']
+texts = [data['text'] for data in train_dataset]
+
+batch_size = 1024
+chunked_embeddings = []
+for i in tqdm(range(0, len(texts), batch_size), desc="Generating embeddings"):
+    batch = texts[i:i+batch_size]
+    embeddings = generate_embeddings_batch(batch)
+    chunked_embeddings.append(embeddings)
+
+chunked_embeddings = np.vstack(chunked_embeddings)
+
+# Initialize FAISS index
+dimension = chunked_embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(chunked_embeddings)
+
+# Initialize Groq client
+client = Groq(api_key="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")
+
+def get_groq_response(query):
+    try:
+        # Get relevant context from FAISS
+        faiss_results = search_in_faiss(query)
+        context = "\n".join([result[0] for result in faiss_results])
+        
+         # Create a prompt that forces the model to only use the provided context
+        prompt = f"""
+        You are an expert assistant for University of Education Lahore and its sub-campuses ONLY.
+        You must ONLY use the following context to answer questions. If the answer isn't in the context,
+        say "I don't have specific information about that in the University of Education Lahore's data provided to me.",
+        and answer the question generally like qrok ai and should feel like real human talking to a real human.
+        
+        Context:
+        {context}
+        
+        Question: {query}
+        
+        Answer:"""
+        
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model="llama3-70b-8192",
+            temperature=0.3,
+            max_tokens=1024
+        )
+        
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        print(f"Error in Groq response: {str(e)}")
+        return "I encountered an error while processing your request."
+
+def search_in_faiss(query):
+    query_embedding = model.encode([query], convert_to_tensor=True, device=device).cpu().numpy()
+    distances, indices = index.search(query_embedding, k=3)
+    return [(dataset['train'][int(idx)]['text'], float(dist)) for idx, dist in zip(indices[0], distances[0])]
+
+def respond(message, chat_history):
+    try:
+        faiss_results = search_in_faiss(message)
+        model_response = get_groq_response(message)
+
+        bot_response = "**Relevant Information from Dataset:**\n\n"
+        for result in faiss_results:
+            bot_response += f"- {result[0]} (Similarity: {result[1]:.4f})\n\n"
+        bot_response = "\n**Model Response:**\n\n" + model_response
+                    #+
+        return "", chat_history + [(message, bot_response)]
+    except Exception as e:
+        print(f"Error: {str(e)}")
+        return "", chat_history + [(message, f"Error processing request: {str(e)}")]
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# <center>UoE Chatbot</center>")
+    gr.Markdown("<center>University of Education Lahore Information Bot</center>")
+    gr.Markdown("<center>This bot only answers questions about University of Education Lahore and its sub-campuses</center>")
+
+    chatbot = gr.Chatbot(height=500, bubble_full_width=False)
+    with gr.Row():
+        msg = gr.Textbox(label="Type your message here...", placeholder="Ask about University of Education Lahore...", scale=7)
+        submit_btn = gr.Button("Submit", variant="primary")
+    clear_btn = gr.Button("Clear Chat")
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    submit_btn.click(respond, [msg, chatbot], [msg, chatbot])
+    clear_btn.click(lambda: None, None, chatbot, queue=False)
+
+demo.launch()