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128
+ </configuration>
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+ <module name="chatbot" />
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+ <option name="REDIRECT_INPUT" value="false" />
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+ <option name="INPUT_FILE" value="" />
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+ <method v="2" />
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+ <configuration name="deploy_to_discord" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
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+ <method v="2" />
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+ </configuration>
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+ <configuration name="gradio deploy discord" type="PythonConfigurationType" factoryName="Python">
176
+ <module name="chatbot" />
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+ <option name="ENV_FILES" value="" />
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+ <module name="chatbot" />
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+ <option name="PARAMETERS" value="deploy --title echo-chatbot --app-file chatbot_messages.py" />
216
+ <option name="SHOW_COMMAND_LINE" value="false" />
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+ <option name="REDIRECT_INPUT" value="false" />
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+ <method v="2" />
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+ </configuration>
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+ <configuration name="gradio" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
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+ <module name="chatbot" />
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+ </project>
README.md CHANGED
@@ -1,12 +1,6 @@
1
  ---
2
- title: Echo Chatbot
3
- emoji: 🏃
4
- colorFrom: indigo
5
- colorTo: yellow
6
  sdk: gradio
7
  sdk_version: 4.44.0
8
- app_file: app.py
9
- pinned: false
10
  ---
11
-
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: echo-chatbot
3
+ app_file: app.py
 
 
4
  sdk: gradio
5
  sdk_version: 4.44.0
 
 
6
  ---
 
 
__pycache__/app.cpython-311.pyc ADDED
Binary file (837 Bytes). View file
 
__pycache__/chatbot.cpython-311.pyc ADDED
Binary file (3.64 kB). View file
 
app.py ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+
3
+
4
+ def greeting(text):
5
+ return f"Hello, {text}!"
6
+
7
+
8
+ interface = gr.Interface(
9
+ fn=greeting,
10
+ inputs=["text"],
11
+ outputs=["text"],
12
+ live=True
13
+ )
14
+
15
+ interface.queue().launch()
16
+
17
+ # demo = gr.ChatInterface(slow_echo).queue().launch()
block.py ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+
3
+ with gr.Blocks() as demo:
4
+ name = gr.Textbox(label="Name")
5
+ output = gr.Textbox(label="Output Box")
6
+ greet_btn = gr.Button("Greet")
7
+
8
+ @greet_btn.click(inputs=name, outputs=output)
9
+ def greet(name):
10
+ return "Hello " + name + "!"
11
+
12
+ demo.launch()
chatbot.py ADDED
@@ -0,0 +1,61 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ import plotly.express as px
3
+
4
+ with gr.Blocks() as demo:
5
+ chatbot = gr.Chatbot()
6
+ msg = gr.Textbox()
7
+ clear = gr.ClearButton([msg, chatbot])
8
+
9
+
10
+ def random_plot():
11
+ df = px.data.iris()
12
+ fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
13
+ size='petal_length', hover_data=['petal_width'])
14
+ return fig
15
+
16
+
17
+ def user(user_message, history):
18
+ print(f"User message: {user_message}, history: {history}")
19
+ next_history_item_with_no_response = [user_message, None]
20
+ return "", history + [next_history_item_with_no_response]
21
+
22
+
23
+ def add_message(history, message):
24
+ for x in message["files"]:
25
+ history.append(((x,), None))
26
+ if message["text"] is not None:
27
+ history.append((message["text"], None))
28
+ return history, gr.MultimodalTextbox(value=None, interactive=False)
29
+
30
+
31
+ def bot(history):
32
+ history[-1][1] = "yay"
33
+ return history
34
+
35
+
36
+ fig = random_plot()
37
+
38
+
39
+ def vote(data: gr.LikeData):
40
+ if data.liked:
41
+ print(f"You liked it, great!")
42
+ else:
43
+ print("Well, I tried.")
44
+
45
+ with gr.Blocks(fill_height=True) as demo:
46
+ chatbot = gr.Chatbot(
47
+ elem_id="chatbot",
48
+ bubble_full_width=False,
49
+ scale=1,
50
+ )
51
+
52
+ chat_input = gr.MultimodalTextbox(interactive=True, file_count="multiple", placeholder="Type something...", show_label=False)
53
+
54
+ chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
55
+ bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
56
+ bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
57
+
58
+ chatbot.like(vote, None, None)
59
+
60
+ if __name__ == "__main__":
61
+ demo.launch()
chatbot_langchain.py ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from huggingface_hub import InferenceClient
3
+
4
+ client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
5
+
6
+
7
+ def chat_greeter(msg, history):
8
+ messages = history + [{"role": "user", "content": msg}]
9
+ response = {"role": "assistant", "content": ""}
10
+ for message in client.chat_completion(messages, max_tokens=512, stream=True, temperature=0.7, top_p=0.95):
11
+ token = message.choices[0].delta.content
12
+ response["content"] += token
13
+ yield response
14
+
15
+
16
+ # with gr.Blocks() as demo:
17
+ # chatbot = gr.Chatbot(type="messages")
18
+ # msg = gr.Textbox()
19
+ # clear = gr.ClearButton([msg, chatbot])
20
+ #
21
+ # msg.submit(chat_greeter, [msg, chatbot], [chatbot])
22
+
23
+ demo = gr.ChatInterface(chat_greeter, type="messages")
24
+ demo.launch()
chatbot_messages.py ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from huggingface_hub import InferenceClient
3
+
4
+ client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
5
+
6
+
7
+ def chat_greeter(msg, history):
8
+ messages = history + [{"role": "user", "content": msg}]
9
+ response = {"role": "assistant", "content": ""}
10
+ for message in client.chat_completion(messages, max_tokens=512, stream=True, temperature=0.7, top_p=0.95):
11
+ token = message.choices[0].delta.content
12
+ response["content"] += token
13
+ yield response
14
+
15
+
16
+ demo = gr.ChatInterface(chat_greeter, type="messages")
17
+ # demo.queue().deploy_discord(to_id="echo-chatbot-discord-bot", discord_bot_token="MTI4NjEzNDcyNTMyODc2OTAyNA.G_vDur.RNHVFJ_KAS8vH6ht06Iz4o21OsEOK2xCVySoHs")
18
+ demo.launch()
19
+
deploy_to_discord.py ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ import gradio_client as grc
2
+
3
+ grc.Client("scornflake/echo-chatbot").deploy_discord(to_id="echo-chatbot-discord-bot", discord_bot_token="MTI4NjEzNDcyNTMyODc2OTAyNA.G_vDur.RNHVFJ_KAS8vH6ht06Iz4o21OsEOK2xCVySoHs")
4
+
flagged/log.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ name,output,flag,username,timestamp
2
+ test,,,,2024-09-19 13:42:44.252965
transcriber.py ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ import numpy as np
3
+ from transformers import pipeline
4
+
5
+ transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
6
+
7
+ # How to do just the last few seconds?
8
+ # What we really want is to work out the last sentences... or transcribe when there is silence (even perhaps 100ms)
9
+ # Then you'd run two pipelines. One for the transcribe and another for which person is speaking. If you detect a switch in person, you'd output the transcriber text.
10
+ #
11
+ # Hmm. Messy. How to handle the interaction of realtime with the transcriber?
12
+
13
+
14
+ def transcribe(stream, new_chunk):
15
+ sr, y = new_chunk
16
+ if y.ndim > 1:
17
+ y = y.mean(axis=1)
18
+
19
+ y = y.astype(np.float32)
20
+ y /= np.max(np.abs(y))
21
+
22
+ if stream is not None:
23
+ stream = np.concatenate([stream, y])
24
+ else:
25
+ stream = y
26
+
27
+ return stream, transcriber({"sampling_rate": sr, "raw": y})["text"]
28
+
29
+
30
+ interface = gr.Interface(
31
+ fn=transcribe,
32
+ inputs=["state", gr.Audio(sources="microphone", label="Audio", streaming=True)],
33
+ outputs=["state", "text"],
34
+ title="Transcribe the Things",
35
+ description="A thing that does stuff",
36
+ live=True
37
+ )
38
+
39
+ interface.queue().launch()
40
+
41
+ # demo = gr.ChatInterface(slow_echo).queue().launch()