new

.idea/gradio_space.iml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,19 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N806" />
+        </list>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="apis.main.models" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12 (gradio_space)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (gradio_space)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/gradio_space.iml" filepath="$PROJECT_DIR$/.idea/gradio_space.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app.py

@@ -1,137 +1,167 @@
-import io
-from threading import Thread
-import random
-import os
-
-import numpy as np
-import spaces
 import gradio as gr
-import torch
-
-from parler_tts import ParlerTTSForConditionalGeneration
-from pydub import AudioSegment
-from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
-from huggingface_hub import InferenceClient
-from streamer import ParlerTTSStreamer
-import time
+import openai
+from openai import OpenAI
+
+# Configure your API keys
+OPENAI_API_KEY = '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'
+PERPLEXITY_API_KEY = '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'
+
+# Setup API clients
+openai.api_key = OPENAI_API_KEY
+perplexity_client = OpenAI(api_key=PERPLEXITY_API_KEY, base_url="https://api.perplexity.ai")
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+
+def search_linkedin_person(name, company):
+    """Search for a person on LinkedIn via Perplexity API."""
+    query = f"fine this person {name} at {company}, try LinkedIn"
+    try:
+        messages = [
+            {"role": "system", "content": "You are an AI assistant. Provide summary of the person."},
+            {"role": "user", "content": query}
+        ]
+        response = perplexity_client.chat.completions.create(
+            model="llama-3.1-sonar-large-128k-online",
+            messages=messages,
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error searching: {str(e)}"
+
+
+def create_multi_block_app():
+    with gr.Blocks() as demo:
+        # Block 1: LinkedIn Search
+        with gr.Column(variant="panel"):
+            gr.Markdown("## LinkedIn Profile Search")
+            with gr.Row():
+                name_input = gr.Textbox(label="Person's Name", placeholder="Enter the name")
+                company_input = gr.Textbox(label="Company", placeholder="Enter the company")
+
+            search_btn = gr.Button("Search Profile")
+            profile_output = gr.Textbox(label="LinkedIn Profile Info", interactive=False)
+
+            search_btn.click(
+                fn=search_linkedin_person,
+                inputs=[name_input, company_input],
+                outputs=profile_output
+            )
+
+        # Block 2: Introductory Email Chatbot
+        with gr.Column(variant="panel"):
+            gr.Markdown("## 1 Email Chatbot")
+
+            # Create Chatbot and Input Elements
+            intro_chatbot = gr.Chatbot(label="Intro Email Generation")
+            intro_msg_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
+            intro_submit_btn = gr.Button("Send")
+
+            # Define the Chatbot Conversation Function
+            def intro_email_conversation(message, history, profile_info):
+                try:
+                    # Format the conversation history for the OpenAI API
+                    formatted_history = [
+                        {"role": "user" if i % 2 == 0 else "assistant", "content": msg[0]}
+                        for i, msg in enumerate(history)
+                    ]
+
+                    # Add the current user message to the conversation
+                    formatted_history.append({"role": "user", "content": message})
+
+                    # Add profile info to guide the assistant
+                    system_message = {
+                        "role": "system",
+                        "content": (
+                            f"You are an AI assistant helping to draft a professional email"
+                            f"to the following individual: {profile_info}. Make it short and engaging."
+                        )
+                    }
+
+                    # Make a request to the OpenAI API
+                    response = client.chat.completions.create(
+                        model="gpt-4",
+                        messages=[system_message] + formatted_history
+                    )
+
+                    # Extract the AI's response
+                    ai_response = response.choices[0].message.content
+
+                    # Append the new message-response pair to the history
+                    history.append([message, ai_response])
+
+                    return history, ""  # Clear the input box
+                except Exception as e:
+                    # Handle exceptions gracefully and append the error message
+                    history.append([message, f"Error: {str(e)}"])
+                    return history, ""
+
+            # Set up the button click behavior
+            intro_submit_btn.click(
+                fn=intro_email_conversation,
+                inputs=[intro_msg_input, intro_chatbot, profile_output],
+                outputs=[intro_chatbot, intro_msg_input]
+            )
+
+
+        # Block 2: Introductory Email Chatbot
+        with gr.Column(variant="panel"):
+            gr.Markdown("## 2 Email Chatbot")
+
+            # Create Chatbot and Input Elements
+            intro_chatbot = gr.Chatbot(label="Intro Email Generation")
+            intro_msg_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
+            intro_submit_btn = gr.Button("Send")
+
+            # Define the Chatbot Conversation Function
+            def intro_email_conversation(message, history, profile_info):
+                try:
+                    # Format the conversation history for the OpenAI API
+                    formatted_history = [
+                        {"role": "user" if i % 2 == 0 else "assistant", "content": msg[0]}
+                        for i, msg in enumerate(history)
+                    ]
+
+                    # Add the current user message to the conversation
+                    formatted_history.append({"role": "user", "content": message})
+
+                    # Add profile info to guide the assistant
+                    system_message = {
+                        "role": "system",
+                        "content": (
+                            f"You are an AI assistant helping to draft a professional email"
+                            f"to the following individual: {profile_info}. introduce my semicondoctor company."
+                        )
+                    }
+
+                    # Make a request to the OpenAI API
+                    response = client.chat.completions.create(
+                        model="gpt-3.5-turbo",
+                        messages=[system_message] + formatted_history
+                    )
+
+                    # Extract the AI's response
+                    ai_response = response.choices[0].message.content
+
+                    # Append the new message-response pair to the history
+                    history.append([message, ai_response])
+
+                    return history, ""  # Clear the input box
+                except Exception as e:
+                    # Handle exceptions gracefully and append the error message
+                    history.append([message, f"Error: {str(e)}"])
+                    return history, ""
+
+            # Set up the button click behavior
+            intro_submit_btn.click(
+                fn=intro_email_conversation,
+                inputs=[intro_msg_input, intro_chatbot, profile_output],
+                outputs=[intro_chatbot, intro_msg_input]
+            )
+
+    return demo
+
+if __name__ == "__main__":
+    app = create_multi_block_app()
+    # app.launch()
+    app.launch(share=True)  # Share your demo with just 1 extra parameter 🚀
 
-device = "cuda:0" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
-torch_dtype = torch.float16 if device != "cpu" else torch.float32
-
-repo_id = "parler-tts/parler_tts_mini_v0.1"
-
-jenny_repo_id = "ylacombe/parler-tts-mini-jenny-30H"
-
-model = ParlerTTSForConditionalGeneration.from_pretrained(
-    jenny_repo_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True
-).to(device)
-
-client = InferenceClient(token=os.getenv("HF_TOKEN"))
-
-tokenizer = AutoTokenizer.from_pretrained(repo_id)
-feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
-
-SAMPLE_RATE = feature_extractor.sampling_rate
-SEED = 42
-
-
-def numpy_to_mp3(audio_array, sampling_rate):
-    # Normalize audio_array if it's floating-point
-    if np.issubdtype(audio_array.dtype, np.floating):
-        max_val = np.max(np.abs(audio_array))
-        audio_array = (audio_array / max_val) * 32767  # Normalize to 16-bit range
-        audio_array = audio_array.astype(np.int16)
-
-    # Create an audio segment from the numpy array
-    audio_segment = AudioSegment(
-        audio_array.tobytes(),
-        frame_rate=sampling_rate,
-        sample_width=audio_array.dtype.itemsize,
-        channels=1
-    )
-
-    # Export the audio segment to MP3 bytes - use a high bitrate to maximise quality
-    mp3_io = io.BytesIO()
-    audio_segment.export(mp3_io, format="mp3", bitrate="320k")
-
-    # Get the MP3 bytes
-    mp3_bytes = mp3_io.getvalue()
-    mp3_io.close()
-
-    return mp3_bytes
-
-
-sampling_rate = model.audio_encoder.config.sampling_rate
-frame_rate = model.audio_encoder.config.frame_rate
-
-
-def generate_response(audio):
-    gr.Info("Transcribing Audio", duration=5)
-    question = client.automatic_speech_recognition(audio).text
-    messages = [{"role": "system", "content": ("You are a magic 8 ball."
-                                               "Someone will present to you a situation or question and your job "
-                                               "is to answer with a cryptic addage or proverb such as "
-                                               "'curiosity killed the cat' or 'The early bird gets the worm'."
-                                               "Keep your answers short and do not include the phrase 'Magic 8 Ball' in your response. If the question does not make sense or is off-topic, say 'Foolish questions get foolish answers.'"
-                                               "For example, 'Magic 8 Ball, should I get a dog?', 'A dog is ready for you but are you ready for the dog?'")},
-                {"role": "user", "content": f"Magic 8 Ball please answer this question -  {question}"}]
-
-    response = client.chat_completion(messages, max_tokens=64, seed=random.randint(1, 5000),
-                                      model="mistralai/Mistral-7B-Instruct-v0.3")
-    response = response.choices[0].message.content.replace("Magic 8 Ball", "")
-    return response, None, None
-
-
-@spaces.GPU
-def read_response(answer):
-    play_steps_in_s = 2.0
-    play_steps = int(frame_rate * play_steps_in_s)
-
-    description = "Jenny speaks at an average pace with a calm delivery in a very confined sounding environment with clear audio quality."
-    description_tokens = tokenizer(description, return_tensors="pt").to(device)
-
-    streamer = ParlerTTSStreamer(model, device=device, play_steps=play_steps)
-    prompt = tokenizer(answer, return_tensors="pt").to(device)
-
-    generation_kwargs = dict(
-        input_ids=description_tokens.input_ids,
-        prompt_input_ids=prompt.input_ids,
-        streamer=streamer,
-        do_sample=True,
-        temperature=1.0,
-        min_new_tokens=10,
-    )
-
-    set_seed(SEED)
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-    start = time.time()
-    for new_audio in streamer:
-        print(
-            f"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds after {time.time() - start} seconds")
-        yield answer, numpy_to_mp3(new_audio, sampling_rate=sampling_rate)
-
-
-with gr.Blocks() as block:
-    gr.HTML(
-        f"""
-        <h1 style='text-align: center;'> Magic 8 Ball 🎱 </h1>
-        <h3 style='text-align: center;'> Ask a question and receive wisdom </h3>
-        <p style='text-align: center;'> Powered by <a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a>
-        """
-    )
-    with gr.Group():
-        with gr.Row():
-            audio_out = gr.Audio(label="Spoken Answer", streaming=True, autoplay=True, loop=False)
-            answer = gr.Textbox(label="Answer")
-            state = gr.State()
-        with gr.Row():
-            audio_in = gr.Audio(label="Speak you question", sources="microphone", type="filepath")
-    with gr.Row():
-        gr.HTML(
-            """<h3 style='text-align: center;'> Examples: 'What is the meaning of life?', 'Should I get a dog?' </h3>""")
-    audio_in.stop_recording(generate_response, audio_in, [state, answer, audio_out]).then(fn=read_response,
-                                                                                          inputs=state,
-                                                                                          outputs=[answer, audio_out])
-
-block.launch()