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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+response.mp3 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,7 +1,7 @@
 ---
-title: Test
-emoji: ⚡
-colorFrom: gray
+title: Gtalkv01
+emoji: 🔥
+colorFrom: yellow
 colorTo: purple
 sdk: gradio
 sdk_version: 3.38.0

TalktoChatGPT1.py

@@ -0,0 +1,80 @@
+import gradio as gr
+import openai
+import pyttsx3
+
+openai.api_key = "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"
+
+# Global variable to hold the chat history, initialise with system role
+conversation = [
+        {"role": "system", "content": "You are an intelligent professor."}
+        ]
+
+# Add your construct_index function here
+def construct_index(directory_path):
+    max_input_size = 4096
+    num_outputs = 512
+    max_chunk_overlap = 20
+    chunk_size_limit = 2048
+
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+
+    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+
+    index.save_to_disk('index.json')
+
+    return index
+
+# Construct the index here
+directory_path = "path_to_your_directory"  # replace with your directory path
+index = construct_index(directory_path)
+
+# transcribe function to record the audio input
+def transcribe(audio):
+    print(audio)
+
+    # Whisper API
+    audio_file = open(audio, "rb")
+    transcript = openai.Audio.transcribe("whisper-1", audio_file)
+
+    print(transcript)
+
+    # append user's input to conversation
+    conversation.append({"role": "user", "content": transcript["text"]})
+
+    response = openai.ChatCompletion.create(
+    model="gpt-3.5-turbo",
+    messages=conversation
+    )
+    
+    print(response)
+
+    # system_message is the response from ChatGPT API
+    system_message = response["choices"][0]["message"]["content"]
+
+    # append ChatGPT response (assistant role) back to conversation
+    conversation.append({"role": "assistant", "content": system_message})
+
+    # Query the index
+    index_answer = index.query(transcript["text"])
+
+    # Text to speech
+    engine = pyttsx3.init()
+    engine.setProperty("rate", 150)
+    engine.setProperty("voice", "english-us")
+    engine.save_to_file(system_message, "response.mp3")
+    engine.runAndWait()
+
+    # return response as text and audio
+    return transcript["text"], system_message, index_answer, "response.mp3"
+
+# Gradio output
+bot = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=[gr.outputs.Textbox(label="Transcribed Text"), gr.outputs.Textbox(label="API Answer"), gr.outputs.Textbox(label="Index Answer"), "audio"],
+)
+bot.launch()

response.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:446d15908fd8f9ef666455e76e148e84595a06d6865293a13893468b2283455d
+size 1276900