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Update app.py
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app.py
CHANGED
@@ -17,8 +17,6 @@ from huggingface_hub import InferenceClient
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import inspect
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import logging
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import shutil
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import numpy as np
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import soundfile as sf
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# Set up basic configuration for logging
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@@ -30,6 +28,7 @@ llama_cloud_api_key = os.environ.get("LLAMA_CLOUD_API_KEY")
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ACCOUNT_ID = os.environ.get("CLOUDFARE_ACCOUNT_ID")
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API_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")
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API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/a17f03e0f049ccae0c15cdcf3b9737ce/ai/run/"
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print(f"ACCOUNT_ID: {ACCOUNT_ID}")
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print(f"CLOUDFLARE_AUTH_TOKEN: {API_TOKEN[:5]}..." if API_TOKEN else "Not set")
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@@ -399,20 +398,13 @@ def summarize_web_results(query: str, search_results: List[Dict[str, str]], conv
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return f"An error occurred during summarization: {str(e)}"
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# Modify the existing respond function to handle both PDF and web search
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def respond(message, history, model, temperature, num_calls, use_web_search, selected_docs
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if audio_input:
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message = transcribe_audio(audio_input)
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logging.info(f"Transcribed audio: {message}")
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if not message.strip():
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return "Please provide a text or audio query.", history
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logging.info(f"User Query: {message}")
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logging.info(f"Model Used: {model}")
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logging.info(f"Selected Documents: {selected_docs}")
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logging.info(f"Use Web Search: {use_web_search}")
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if use_web_search:
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original_query = message
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@@ -433,9 +425,10 @@ def respond(message, history, model, temperature, num_calls, use_web_search, sel
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if final_summary:
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conversation_manager.add_interaction(original_query, final_summary)
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else:
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else:
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# Existing PDF search logic
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try:
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relevant_docs = [doc for doc in all_relevant_docs if doc.metadata["source"] in selected_docs]
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if not relevant_docs:
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yield partial_response
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else:
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yield partial_response
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except Exception as e:
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logging.error(f"Error with {model}: {str(e)}")
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if "microsoft/Phi-3-mini-4k-instruct" in model:
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logging.info("Falling back to Mistral model due to Phi-3 error")
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fallback_model = "mistralai/Mistral-7B-Instruct-v0.3"
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else:
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history[-1] = (message, response)
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return response, history
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except Exception as e:
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logging.error(f"Error in respond: {str(e)}")
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error_message = f"An error occurred: {str(e)}"
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history[-1] = (message, error_message)
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return error_message, history
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logging.basicConfig(level=logging.DEBUG)
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def get_response_from_cloudflare(prompt, context, query, num_calls=3, temperature=0.2, search_type="pdf"):
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logging.info("Finished generating response")
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def
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# Load the audio file
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audio, sample_rate = sf.read(audio_file)
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audio = audio.mean(axis=1)
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audio = audio.astype(np.int16)
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# Transcribe
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result = client.automatic_speech_recognition(audio, sampling_rate=sample_rate)
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return result["text"]
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def vote(data: gr.LikeData):
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if data.liked:
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@@ -692,96 +673,40 @@ use_web_search = gr.Checkbox(label="Use Web Search", value=False)
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custom_placeholder = "Ask a question (Note: You can toggle between Web Search and PDF Chat in Additional Inputs below)"
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demo = gr.Interface(
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fn=respond,
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inputs=[
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gr.Textbox(placeholder=custom_placeholder, container=False, scale=7),
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gr.State([]), # for history
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gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[3]),
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gr.Slider(minimum=0.1, maximum=1.0, value=0.2, step=0.1, label="Temperature"),
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gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls"),
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gr.Checkbox(label="Use Web Search", value=True),
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gr.CheckboxGroup(label="Select documents to query"),
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gr.Audio(sources="microphone", type="filepath")
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],
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outputs=[gr.Chatbot()],
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title="AI-powered PDF Chat and Web Search Assistant",
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description="Chat with your PDFs or use web search to answer questions. You can type or speak your query.",
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theme=gr.themes.Soft(
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primary_hue="orange",
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secondary_hue="amber",
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neutral_hue="gray",
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font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]
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).set(
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body_background_fill_dark="#0c0505",
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block_background_fill_dark="#0c0505",
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block_border_width="1px",
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block_title_background_fill_dark="#1b0f0f",
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input_background_fill_dark="#140b0b",
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button_secondary_background_fill_dark="#140b0b",
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border_color_accent_dark="#1b0f0f",
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border_color_primary_dark="#1b0f0f",
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background_fill_secondary_dark="#0c0505",
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color_accent_soft_dark="transparent",
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code_background_fill_dark="#140b0b"
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),
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css=css,
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examples=[
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["Tell me about the contents of the uploaded PDFs."],
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["What are the main topics discussed in the documents?"],
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["Can you summarize the key points from the PDFs?"],
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["What's the latest news about artificial intelligence?"]
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],
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cache_examples=False,
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analytics_enabled=False,
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)
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#
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# Add file upload functionality
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with gr.Blocks() as demo:
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likeable=True,
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layout="bubble",
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height=400,
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value=initial_conversation()
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)
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state = gr.State([])
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with gr.Row():
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)
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with gr.Accordion("⚙️ Parameters", open=False):
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model = gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[3])
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temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.2, step=0.1, label="Temperature")
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num_calls = gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls")
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use_web_search = gr.Checkbox(label="Use Web Search", value=True)
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submit_button = gr.Button("Submit")
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submit_button.click(
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fn=respond,
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inputs=[
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text_input,
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state,
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model,
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temperature,
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num_calls,
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use_web_search,
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selected_docs,
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audio_input
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],
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outputs=[chatbot, state]
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)
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# Add file upload functionality
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gr.Markdown("## Upload and Manage PDF Documents")
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with gr.Row():
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update_output = gr.Textbox(label="Update Status")
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delete_button = gr.Button("Delete Selected Documents")
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update_button.click(
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update_vectors,
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inputs=[file_input, parser_dropdown],
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outputs=[update_output,
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)
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refresh_button.click(
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refresh_documents,
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inputs=[],
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outputs=[
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)
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delete_button.click(
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delete_documents,
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inputs=[
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outputs=[update_output,
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)
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gr.Markdown(
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"""
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## How to use
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"""
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)
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if __name__ == "__main__":
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demo.launch(share=True)
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import inspect
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import logging
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import shutil
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# Set up basic configuration for logging
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ACCOUNT_ID = os.environ.get("CLOUDFARE_ACCOUNT_ID")
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API_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")
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API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/a17f03e0f049ccae0c15cdcf3b9737ce/ai/run/"
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whisper_api = InferenceClient("openai/whisper-small", token=huggingface_token)
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print(f"ACCOUNT_ID: {ACCOUNT_ID}")
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print(f"CLOUDFLARE_AUTH_TOKEN: {API_TOKEN[:5]}..." if API_TOKEN else "Not set")
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return f"An error occurred during summarization: {str(e)}"
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# Modify the existing respond function to handle both PDF and web search
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def respond(message, history, model, temperature, num_calls, use_web_search, selected_docs):
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logging.info(f"User Query: {message}")
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logging.info(f"Model Used: {model}")
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logging.info(f"Selected Documents: {selected_docs}")
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logging.info(f"Use Web Search: {use_web_search}")
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response = ""
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if use_web_search:
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original_query = message
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if final_summary:
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conversation_manager.add_interaction(original_query, final_summary)
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response = final_summary
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else:
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response = "Unable to generate a response. Please try a different query."
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else:
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# Existing PDF search logic
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try:
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relevant_docs = [doc for doc in all_relevant_docs if doc.metadata["source"] in selected_docs]
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if not relevant_docs:
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response = "No relevant information found in the selected documents. Please try selecting different documents or rephrasing your query."
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else:
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context_str = "\n".join([doc.page_content for doc in relevant_docs])
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logging.info(f"Context length: {len(context_str)}")
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if model.startswith("duckduckgo/"):
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# Use DuckDuckGo chat with context
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for partial_response in get_response_from_duckduckgo(message, model, context_str, num_calls, temperature):
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response += partial_response
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elif model == "@cf/meta/llama-3.1-8b-instruct":
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# Use Cloudflare API
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for partial_response in get_response_from_cloudflare(prompt="", context=context_str, query=message, num_calls=num_calls, temperature=temperature, search_type="pdf"):
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response += partial_response
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else:
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# Use Hugging Face API
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for partial_response in get_response_from_pdf(message, model, selected_docs, num_calls=num_calls, temperature=temperature):
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response += partial_response
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else:
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response = "No documents available. Please upload PDF documents to answer questions."
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except Exception as e:
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logging.error(f"Error with {model}: {str(e)}")
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if "microsoft/Phi-3-mini-4k-instruct" in model:
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logging.info("Falling back to Mistral model due to Phi-3 error")
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fallback_model = "mistralai/Mistral-7B-Instruct-v0.3"
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return respond(message, history, fallback_model, temperature, num_calls, use_web_search, selected_docs)
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else:
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response = f"An error occurred with the {model} model: {str(e)}. Please try again or select a different model."
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# Update the conversation history
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history.append((message, response))
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# Yield the updated history
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yield history
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logging.basicConfig(level=logging.DEBUG)
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def get_response_from_cloudflare(prompt, context, query, num_calls=3, temperature=0.2, search_type="pdf"):
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logging.info("Finished generating response")
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def transcribe(audio_file):
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if audio_file is None:
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return ""
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with open(audio_file, "rb") as f:
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audio_data = f.read()
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response = whisper_api(audio_data)
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return response["text"]
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def vote(data: gr.LikeData):
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if data.liked:
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custom_placeholder = "Ask a question (Note: You can toggle between Web Search and PDF Chat in Additional Inputs below)"
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def update_textbox(transcription):
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return gr.Textbox.update(value=transcription)
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# Update the Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown("# AI-powered PDF Chat and Web Search Assistant with Speech Input")
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with gr.Row():
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with gr.Column(scale=1):
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audio_input = gr.Audio(sources="microphone", type="filepath", label="Speak your query")
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transcribe_button = gr.Button("Transcribe")
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with gr.Column(scale=2):
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chatbot = gr.Chatbot(
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show_copy_button=True,
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likeable=True,
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layout="bubble",
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height=400,
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value=initial_conversation()
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)
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query_textbox = gr.Textbox(
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placeholder="Ask a question about the uploaded PDFs or any topic",
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container=False,
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scale=7
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)
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submit_button = gr.Button("Submit")
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with gr.Accordion("⚙️ Parameters", open=False):
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model = gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[3])
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temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.2, step=0.1, label="Temperature")
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num_calls = gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of API Calls")
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use_web_search = gr.Checkbox(label="Use Web Search", value=True)
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document_selector = gr.CheckboxGroup(label="Select documents to query")
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# Add file upload functionality
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gr.Markdown("## Upload and Manage PDF Documents")
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with gr.Row():
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update_output = gr.Textbox(label="Update Status")
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delete_button = gr.Button("Delete Selected Documents")
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# Connect components
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transcribe_button.click(
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transcribe,
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inputs=[audio_input],
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outputs=[query_textbox]
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)
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+
submit_button.click(
|
729 |
+
respond,
|
730 |
+
inputs=[query_textbox, chatbot, model, temperature, num_calls, use_web_search, document_selector],
|
731 |
+
outputs=[chatbot]
|
732 |
+
)
|
733 |
+
|
734 |
update_button.click(
|
735 |
update_vectors,
|
736 |
inputs=[file_input, parser_dropdown],
|
737 |
+
outputs=[update_output, document_selector]
|
738 |
)
|
739 |
|
740 |
refresh_button.click(
|
741 |
refresh_documents,
|
742 |
inputs=[],
|
743 |
+
outputs=[document_selector]
|
744 |
)
|
745 |
|
746 |
delete_button.click(
|
747 |
delete_documents,
|
748 |
+
inputs=[document_selector],
|
749 |
+
outputs=[update_output, document_selector]
|
750 |
)
|
751 |
|
752 |
gr.Markdown(
|
753 |
"""
|
754 |
## How to use
|
755 |
+
1. Use the microphone to speak your query, then click "Transcribe", or type directly in the text box.
|
756 |
+
2. Click "Submit" to get a response from the AI.
|
757 |
+
3. Upload PDF documents using the file input at the bottom.
|
758 |
+
4. Select the PDF parser (pypdf or llamaparse) and click "Upload Document" to update the vector store.
|
759 |
+
5. Select the documents you want to query using the checkboxes.
|
760 |
+
6. Toggle "Use Web Search" to switch between PDF chat and web search.
|
761 |
+
7. Adjust Temperature and Number of API Calls to fine-tune the response generation.
|
762 |
"""
|
763 |
)
|
764 |
+
|
765 |
if __name__ == "__main__":
|
766 |
+
demo.launch(share=True)
|
767 |
+
Troubleshooting Python Audio Recording Issues - Claude
|