#5 initial app committed

Dockerfile

@@ -1,14 +1,17 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-
+# Base image
 FROM python:3.9
 
+# Set the working directory inside the container
 WORKDIR /code
 
+# Copy the dependencies file to the working directory
 COPY ./requirements.txt /code/requirements.txt
 
+# Install any dependencies
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
+# Copy the content of the local src directory to the working directory
 COPY . .
 
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+# Specify the command to run on container start
+CMD ["python", "app.py", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import requests
+import json
+import os
+
+API_TOKEN = os.getenv("HF_API_TOKEN")
+TRANSCRIBE_API_URL = "https://api-inference.huggingface.co/models/facebook/wav2vec2-base-960h"
+LLM_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
+
+def transcribe_audio(audio_file):
+    """Transcribe audio file to text."""
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    with open(audio_file, "rb") as f:
+        data = f.read()
+    response = requests.post(TRANSCRIBE_API_URL, headers=headers, data=data)
+    transcription = json.loads(response.content.decode("utf-8")).get("text", "Transcription not available")
+    return transcription
+
+def get_answer(context, question):
+    """Get an answer from the LLM based on the context and question."""
+    prompt = f"Context: {context}\nQuestion: {question}\nAnswer:"
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    response = requests.post(LLM_API_URL, headers=headers, json={"inputs": prompt})
+    answer = json.loads(response.content.decode("utf-8"))[0].get("generated_text", "Answer not available")
+    return answer
+
+def transcribe_and_answer(audio_file, question):
+    """Process the audio file for transcription and use the result to get an answer to a question."""
+    transcription = transcribe_audio(audio_file)
+    answer = get_answer(transcription, question)
+    return transcription, answer
+
+# Create the Gradio app
+with gr.Blocks() as app:
+    gr.Markdown("### Audio to Text and Q&A Chatbot")
+    with gr.Row():
+        # Corrected 'type' parameter value to 'filepath'
+        audio_input = gr.Audio(type="filepath", label="Upload your audio question")
+        question_input = gr.Textbox(label="Type your question here")
+    answer_button = gr.Button("Get Answer")
+    with gr.Row():
+        transcription_output = gr.Textbox(label="Transcription")
+        answer_output = gr.Textbox(label="Answer")
+    
+    answer_button.click(transcribe_and_answer, inputs=[audio_input, question_input], outputs=[transcription_output, answer_output])
+
+
+if __name__ == "__main__":
+    app.launch()
+
+
+
+

main.py

@@ -1,7 +0,0 @@
-from fastapi import FastAPI
-
-app = FastAPI()
-
-@app.get("/")
-def read_root():
-    return {"Hello": "World!"}