Added Sample Demo to README.md

README.md

@@ -18,7 +18,7 @@ Group :
 
 ## Project Brief
 
-The main aim of this project was to make a conversation bot able to take in audio input and output a meaningful reply. 
+The main aim of this project was to make a conversation bot able to take in audio input and output a meaningful reply.
 
 The three main parts of this project were:
 
@@ -40,15 +40,15 @@ This model is implemented to convert the audio messages of the user into text.
 
 This model is implemtented to conver the response generation part of the conversational bot.
 
-## ENCODER 
+## ENCODER
 
 ![encoder](https://user-images.githubusercontent.com/56124350/85904325-0c215300-b826-11ea-9312-e8ccd9cb2ce1.png)
 
-## DECODER 
+## DECODER
 
 ![decoder](https://user-images.githubusercontent.com/56124350/85904328-0e83ad00-b826-11ea-9f48-179de5c00319.png)
 
-## LDA MODEL 
+## LDA MODEL
 
 This model is implemented to add topic awareness to ENCODER - DECODER Model for better response generation.
 
@@ -60,39 +60,41 @@ This graph shows the optimal number of topics we need to set for news articles d
 
 ## Gensim LDA Model parameters
 
-* **corpus** —   Stream of document vectors or sparse matrix of shape (num_terms, num_documents) <
-* **id2word** – Mapping from word IDs to words. It is used to determine the vocabulary size, as well as for debugging and topic printing.
-* **num_topics** — The number of requested latent topics to be extracted from the training corpus.
-* **random_state** — Either a randomState object or a seed to generate one. Useful for reproducibility.
-* **update_every** — Number of documents to be iterated through for each update. Set to 0 for batch learning, > 1 for online iterative learning.
-* **chunksize** — Number of documents to be used in each training chunk.
-* **passes** — Number of passes through the corpus during training.
-* **alpha** — auto: Learns an asymmetric prior from the corpus
-* **per_word_topics** — If True, the model also computes a list of topics, sorted in descending order of most likely topics for each word, along with their phi values multiplied by the feature-length (i.e. word count)
-
+- **corpus** — Stream of document vectors or sparse matrix of shape (num_terms, num_documents) <
+- **id2word** – Mapping from word IDs to words. It is used to determine the vocabulary size, as well as for debugging and topic printing.
+- **num_topics** — The number of requested latent topics to be extracted from the training corpus.
+- **random_state** — Either a randomState object or a seed to generate one. Useful for reproducibility.
+- **update_every** — Number of documents to be iterated through for each update. Set to 0 for batch learning, > 1 for online iterative learning.
+- **chunksize** — Number of documents to be used in each training chunk.
+- **passes** — Number of passes through the corpus during training.
+- **alpha** — auto: Learns an asymmetric prior from the corpus
+- **per_word_topics** — If True, the model also computes a list of topics, sorted in descending order of most likely topics for each word, along with their phi values multiplied by the feature-length (i.e. word count)
 
 ## About pyLDAvis
 
-* The size of the bubbles tells us how dominant a topic is across all the documents (our corpus)
-* The words on the right are the keywords driving that topic
-* The closer the bubbles the more similar the topic. The farther they are apart the less similar
-* Preferably, we want non-overlapping bubbles as much as possible spread across the chart.
-
+- The size of the bubbles tells us how dominant a topic is across all the documents (our corpus)
+- The words on the right are the keywords driving that topic
+- The closer the bubbles the more similar the topic. The farther they are apart the less similar
+- Preferably, we want non-overlapping bubbles as much as possible spread across the chart.
 
 ## Text to Audio
 
 [gTTs](https://pypi.org/project/gTTS/), a python library was used to make a function to output audio from the generated responses.
 
-
 ---
 
-## Installation and usage 
+## Installation and usage
 
 The requirements are in the requirements.txt file.
-* `pip install -r requirements.txt `
-* `python3 -m spacy download en`
+
+- `pip install -r requirements.txt`
+- `python3 -m spacy download en`
 
 The main file o focus is tf_attention_model.py.
 
 To run the bot ->
 `python3 tf_attention_model.py -d False`
+
+## Demo
+
+![Sample Demo](sample2.png "Sample")