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sinatayebati
commited on
Commit
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Parent(s):
c6872be
#7 talking duck v0.1 committed
Browse files- Dockerfile +1 -1
- app.py +72 -6
- docker-compose.yml +9 -0
- helper.txt +19 -0
Dockerfile
CHANGED
@@ -14,4 +14,4 @@ RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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COPY . .
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# Specify the command to run on container start
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CMD ["python", "app.py"
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COPY . .
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# Specify the command to run on container start
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CMD ["python", "app.py"]
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app.py
CHANGED
@@ -4,7 +4,7 @@ import json
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import os
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API_TOKEN = os.getenv("HF_API_TOKEN")
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TRANSCRIBE_API_URL = "https://api-inference.huggingface.co/models/
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LLM_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
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def transcribe_audio(audio_file):
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@@ -18,23 +18,88 @@ def transcribe_audio(audio_file):
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def get_answer(context, question):
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"""Get an answer from the LLM based on the context and question."""
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prompt =
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headers = {"Authorization": f"Bearer {API_TOKEN}"}
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answer = json.loads(response.content.decode("utf-8"))[0].get("generated_text", "Answer not available")
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return answer
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def transcribe_and_answer(audio_file, question):
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"""Process the audio file for transcription and use the result to get an answer to a question."""
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transcription = transcribe_audio(audio_file)
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answer = get_answer(transcription, question)
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return transcription, answer
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# Create the Gradio app
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with gr.Blocks() as app:
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gr.
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with gr.Row():
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# Corrected 'type' parameter value to 'filepath'
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audio_input = gr.Audio(type="filepath", label="Upload your audio question")
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question_input = gr.Textbox(label="Type your question here")
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answer_button = gr.Button("Get Answer")
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@@ -45,8 +110,9 @@ with gr.Blocks() as app:
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answer_button.click(transcribe_and_answer, inputs=[audio_input, question_input], outputs=[transcription_output, answer_output])
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if __name__ == "__main__":
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app.launch()
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import os
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API_TOKEN = os.getenv("HF_API_TOKEN")
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TRANSCRIBE_API_URL = "https://api-inference.huggingface.co/models/openai/whisper-base.en"
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LLM_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
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def transcribe_audio(audio_file):
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def get_answer(context, question):
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"""Get an answer from the LLM based on the context and question."""
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prompt = (
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"As an intelligent coding assistant, your task is to provide clear, concise, and accurate answers to coding-related questions. "
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"Below are examples of questions and the kind of direct answers expected:\n\n"
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"Example Question 1: How can I remove duplicates from a list in Python?\n"
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"Example Answer 1: Use the set() function to convert the list to a set, which removes duplicates, then convert it back to a list.\n\n"
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"Example Question 2: What's the difference between '==' and '===' in JavaScript?\n"
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"Example Answer 2: '==' checks for equality of values after type coercion, while '===' checks for both value and type equality without coercion.\n\n"
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"Example Question 3: How to check if a key exists in a dictionary in Python?\n"
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"Example Answer 3: Use the 'in' keyword, like 'if key in my_dict:'.\n\n"
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"Based on the above examples, answer the following question:\n\n"
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f"Question: {question}\n"
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"Answer:"
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)
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headers = {"Authorization": f"Bearer {API_TOKEN}"}
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# Adjust generation parameters for more focused and relevant responses
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payload = {
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"inputs": prompt,
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"parameters": {
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"temperature": 0.3, # More deterministic
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"top_p": 0.95, # Consider top 90% probable tokens at each step
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"max_new_tokens": 100, # Limit the response length
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"repetition_penalty": 1.2, # Discourage repetition
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"num_return_sequences": 1, # Number of responses to generate
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"return_full_text": False, # Return only generated text, not the full prompt
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"top_k" : 50,
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"truncate" : 24576,
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"max_new_tokens" : 8192,
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"stop" : ["</s>"]
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},
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"options": {
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"use_cache": True # Use cached responses when available
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}
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}
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response = requests.post(LLM_API_URL, headers=headers, json=payload)
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answer = json.loads(response.content.decode("utf-8"))[0].get("generated_text", "Answer not available")
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return answer
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def transcribe_and_answer(audio_file, question):
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"""Process the audio file for transcription and use the result to get an answer to a question."""
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transcription = transcribe_audio(audio_file)
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answer = get_answer(transcription, question)
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return transcription, answer
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# Create the Gradio app
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import gradio as gr
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# Create the Gradio app
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with gr.Blocks() as app:
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gr.HTML("""
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<div style="display: flex; align-items: center; justify-content: center; margin-bottom: 20px;">
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<img src="https://huggingface.co/spaces/sinatayebati/Talking-Duck/resolve/main/assets/talking-duck-logo.webp" alt="Talking Duck Logo" style="width: 120px;"/>
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<div style="margin-left: 20px;">
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<h1 style="font-weight: bold; font-size: 32px; margin: 0;">TALKING DUCK</h1>
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<h3 style="margin: 0;">An Audio to Text Q&A Chatbot</h3>
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</div>
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</div>
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<p style="text-align: center;">Your swift coding sidekick. Speak your code queries, and let the duck do the magic.</p>
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""")
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gr.Markdown("""
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<div style="background-color: #0A192F; color: white; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
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<div style="font-size: 16px; font-weight: bold; text-align: center; margin-bottom: 10px;">Models running on backend</div>
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<div style="display: flex; justify-content: space-around; align-items: center;">
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<div>
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<img src="https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/mistral-logo.png" alt="Mistral Logo" style="width: 40px; margin-bottom: 10px;"/>
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<div style="font-size: 14px;">mistralai/Mistral-7B-v0.1</div>
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<a href="https://huggingface.co/mistralai/Mistral-7B-v0.1" target="_blank" style="color: white; text-decoration: none; font-size: 12px;">Model Page</a>
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</div>
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<div>
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<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1620805164087-5ec0135ded25d76864d553f1.png?w=200&h=200&f=face" alt="Second Model Logo" style="width: 40px; margin-bottom: 10px;"/>
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<div style="font-size: 14px;">openai/whisper-base.en</div>
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<a href="https://huggingface.co/openai/whisper-base.en" target="_blank" style="color: white; text-decoration: none; font-size: 12px;">Model Page</a>
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</div>
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</div>
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</div>
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""")
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with gr.Row():
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audio_input = gr.Audio(type="filepath", label="Upload your audio question")
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question_input = gr.Textbox(label="Type your question here")
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answer_button = gr.Button("Get Answer")
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answer_button.click(transcribe_and_answer, inputs=[audio_input, question_input], outputs=[transcription_output, answer_output])
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if __name__ == "__main__":
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app.launch(server_name="0.0.0.0")
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docker-compose.yml
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@@ -0,0 +1,9 @@
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# Example docker-compose.yml for Gradio app
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version: '3'
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services:
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gradio-app:
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build: .
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ports:
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- "7860:7860"
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environment:
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- HF_API_TOKEN
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helper.txt
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@@ -0,0 +1,19 @@
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# Ensure Docker Buildx is used and setup
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docker buildx create --use
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# Build your Docker image with Buildx (assuming you're now working locally and not pushing to a registry)
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docker buildx build --load --secret id=HF_API_TOKEN,env=HF_API_TOKEN -t gradio-app .
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# Now run your Docker container, passing the secret environment variable
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docker run -it -p 7860:7860 -e HF_API_TOKEN=$HF_API_TOKEN gradio-app
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docker build -t gradio-app .
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docker run -it -p 7860:7860 -e HF_API_TOKEN=$HF_API_TOKEN gradio-app
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docker-compose up --build
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"--host", "0.0.0.0", "--port", "7860"
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