Update interaction.py

interaction.py

@@ -1,12 +1,73 @@
-import requests
 from gtts import gTTS
 import base64
 import os
+
 from haystack import Document
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.components.builders import PromptBuilder
+from haystack.components.generators.hugging_face_local import HuggingFaceLocalGenerator
+from haystack.pipeline import Pipeline
 
-def init_doc_store():
+def init_doc_store(path, files):
     docs = []
     for file in files:
         with open(path + '/' + file, 'r') as f:
             content = f.read()
-            docs.append(Document(content=content, meta={'name':file}))
+            docs.append(Document(content=content, meta={'name':file}))
+
+    document_store = InMemoryDocumentStore()
+    document_store.write_documents(docs)
+    return document_store
+
+def define_components(document_store):
+    retriever = InMemoryBM25Retriever(document_store, top_k=3)
+    
+    template = """
+    Given the following information, answer the question.
+    
+    Context:
+    {% for document in documents %}
+        {{ document.content }}
+    {% endfor %}
+    
+    Question: {{question}}
+    Answer:
+    """
+    prompt_builder = PromptBuilder(template=template)
+    
+    generator = HuggingFaceLocalGenerator(model="gpt2",
+                                          task="text-generation",
+                                        #   device='cuda',
+                                          generation_kwargs={
+                                            "max_new_tokens": 100,
+                                            "temperature": 0.9,
+                                            })
+    generator.warm_up()
+    return retreiver, prompt_builder, generator
+
+def define_pipeline(retreiver, prompt_builder, generator):
+    basic_rag_pipeline = Pipeline()
+    
+    basic_rag_pipeline.add_component("retriever", retriever)
+    basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
+    basic_rag_pipeline.add_component("llm", generator)
+    
+    basic_rag_pipeline.connect("retriever", "prompt_builder.documents")
+    basic_rag_pipeline.connect("prompt_builder", "llm")
+
+    return basic_rag_pipeline
+
+def generate_response(question, pipeline):
+	response = pipeline.run({'retriever':{"query":question}, 'prompt_builder':{'question':question}})
+    response = response['llm']['replies'][0]
+    return response
+def audio_response(response):
+	audio_stream="response_audio.mp3"
+	tts = gTTS(response)
+	tts.save(audio_stream)
+	with open(audio_stream, 'rb') as file:
+		audio_data = file.read()
+	audio_base64 = base64.b64encode(audio_data).decode('utf-8')
+	audio_tag = f'<audio autoplay="true" src="data:audio/mp3;base64,{audio_base64}">'
+	return audio_tag