Update app.py

app.py

@@ -5,20 +5,42 @@ import torch
 # Load the sentence-transformer model
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
-# Read the contents of the uploaded FAQ text file
-with open("faq.txt", "r") as file:
-    corpus = file.readlines()
+# Expanded FAQ dataset with more diverse responses
+faq_data = [
+    ("What is Hugging Face?", "Hugging Face is a company specializing in AI and machine learning, known for their open-source models and datasets."),
+    ("How does Hugging Face help in AI?", "Hugging Face provides tools, libraries, and pre-trained models to make machine learning easier and more accessible."),
+    ("What is machine learning?", "Machine learning is a subset of AI that enables computers to learn from data and improve over time without being explicitly programmed."),
+    ("What is a transformer model?", "A transformer model is a deep learning model that uses attention mechanisms to process and generate sequences of data, such as text or speech."),
+    ("Can I use Hugging Face models in production?", "Yes, Hugging Face provides tools and frameworks like `transformers` for deploying models into production environments."),
+    ("What is RAG?", "Retrieval-Augmented Generation (RAG) combines pre-trained models with retrieval systems to answer questions using both the knowledge from the model and external documents."),
+    ("What is AI?", "Artificial Intelligence (AI) is the simulation of human intelligence in machines, enabling them to perform tasks that typically require human cognition."),
+    ("Tell me a joke", "Why don't skeletons fight each other? They don’t have the guts!"),
+    ("What is the capital of France?", "The capital of France is Paris."),
+    ("How can I contact support?", "You can contact support via our website or email for assistance."),
+    ("What's the weather like today?", "Sorry, I don't have access to real-time data, but I suggest checking a weather app for the latest updates.")
+]
 
-# Encode the corpus
+# Encode the FAQ dataset
+corpus = [item[0] for item in faq_data]  # Questions only
+answers = {item[0]: item[1] for item in faq_data}  # Map questions to their answers
 corpus_embeddings = model.encode(corpus, convert_to_tensor=True)
 
-# Function to retrieve relevant document based on user query
+# Function to retrieve the most relevant FAQ based on user query
 def retrieve(query):
     query_embedding = model.encode(query, convert_to_tensor=True)
     cosine_scores = util.pytorch_cos_sim(query_embedding, corpus_embeddings)[0]
-    top_result = torch.argmax(cosine_scores)
-    return corpus[top_result]
+    top_result_idx = torch.argmax(cosine_scores)
+    top_score = cosine_scores[top_result_idx].item()
+
+    # Threshold for similarity score
+    if top_score < 0.5:  # Adjust threshold as needed
+        return "I didn’t understand that. Could you try asking something else?"
+
+    # Return the most relevant answer based on the query
+    return answers[corpus[top_result_idx]]
 
 # Create Gradio interface
-iface = gr.Interface(fn=retrieve, inputs="text", outputs="text", live=True)
+iface = gr.Interface(fn=retrieve, inputs="text", outputs="text", live=True, title="RAG AI Bot", description="Ask me anything related to Hugging Face or general knowledge!")
+
+# Launch the Gradio interface
 iface.launch()