Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1,11 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+.env

.gradio/certificate.pem

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.python-version

@@ -0,0 +1 @@
+3.13

README.md

@@ -1,12 +1,138 @@
----
-title: Customer Support Agent
-emoji: 📉
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 5.23.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Customer_Support_Agent
+app_file: app.py
+sdk: gradio
+sdk_version: 5.23.3
+---
+
+# 💬 Customer Support Assistant
+
+An AI-powered voice + text chatbot built with [`pydantic_ai`](https://github.com/roboflow/pydantic-ai), powered by Llama 3.3 70B via Cerebras/Groq. It processes customer issues, detects emotional tone, and records support requests into a structured data table. Deployable on Gradio Spaces and usable locally with both text and audio input.
+
+---
+
+## ⚡ Features
+
+- 🔥 **LLM-powered form extraction** using `Agent` abstraction from `pydantic_ai`
+- 🎙️ **Voice chat** with real-time streaming via [`fastrtc`](https://github.com/Rikhil-Rai/fastrtc)
+- 🧠 **Memory-aware responses** using `message_history`
+- 📊 **Live DataFrame updates** for structured customer requests
+- 💾 **Persistent CSV logging**
+- 🛠️ One-click Gradio UI with tabs for Chat + Customer Data
+
+---
+
+## 🚀 Getting Started
+
+### 1. Clone the Repo
+
+```bash
+git clone https://github.com/your-username/customer-support-assistant
+cd customer-support-assistant
+```
+
+### 2. Install Dependencies
+
+> ⚠️ Make sure you use `uv` to ensure proper dependency resolution (especially for `pydantic_ai`).
+
+```bash
+uv pip install -r requirements.txt
+```
+
+> Or use `uv` directly:
+
+```bash
+uv venv
+source .venv/bin/activate
+uv pip install -r requirements.txt
+```
+
+---
+
+### 3. Setup `.env` File
+
+Create a `.env` file in the root directory.
+
+Refer to `settings.py` for required fields. At a minimum, you will need:
+
+```env
+CEREBRAS_API_KEY=your_api_key
+CEREBRAS_BASE_URL=https://api.groq.com/openai/v1
+```
+
+You can optionally set environment variables for your own STT/TTS models as required by `fastrtc`.
+
+---
+
+### 4. Run the App Locally
+
+```bash
+python app.py
+```
+
+---
+
+## 🌐 Deploy on Gradio Spaces
+
+1. Add your `CEREBRAS_API_KEY` and `CEREBRAS_BASE_URL` as secrets in the Gradio Space.
+2. Make sure `fastrtc` audio support is configured in the hardware tab.
+3. Gradio Spaces will auto-launch the app via `app.py`.
+
+---
+
+## 🧪 Debug Tips
+
+To simulate extraction alone:
+
+```bash
+python agents.py
+```
+
+Then type messages in the CLI to test how well the form is filled.
+
+---
+
+## 📁 File Structure
+
+```plaintext
+├── app.py                 # Main Gradio UI
+├── agents.py              # Agent config + LLM interaction logic
+├── settings.py            # Handles env/config
+├── form_prompt.txt        # System prompt for form extraction
+├── response_prompt.txt    # System prompt for response generation
+├── data.csv               # Auto-generated CSV storage
+├── requirements.txt
+├── README.md
+```
+
+---
+
+## 🧠 What the AI Extracts
+
+The form agent will pull out:
+
+- `customername`: Extracted from input or marked as `"unknown"`
+- `requesttype`: e.g., `"billing"`, `"technical support"`
+- `issue`: 50-line description of the issue
+- `emotion`: `"angry"`, `"happy"`, etc.
+
+---
+
+## 🛑 Known Limitations
+
+- Longform audio may require silence-based segmentation tuning.
+- This project assumes inputs are customer support related. General queries may misfire.
+- Error handling is basic — add guards if scaling for production use.
+
+---
+
+## 🙏 Credits
+
+- Built using [pydantic_ai](https://github.com/roboflow/pydantic-ai)
+- Voice support via [fastrtc](https://github.com/Rikhil-Rai/fastrtc)
+- LLM backend: Llama 3.3 70B via [Groq](https://groq.com/)
+- UI powered by [Gradio](https://gradio.app/)
+
+---
+
+## ❤️ Made with care by Rikhil

agents.py

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+from pydantic_ai import Agent, RunContext
+import openai
+from pydantic_ai.models.openai import OpenAIModelSettings, OpenAIModel
+from pydantic_ai.providers.openai import OpenAIProvider
+from pydantic import BaseModel, Field
+from settings import Settings
+import asyncio
+from dataclasses import dataclass
+
+settings = Settings()
+
+groq_settings = OpenAIModelSettings(
+    temperature=0.7,
+    top_p=0.95,
+    frequency_penalty=0,
+)
+
+model_name = "llama-3.3-70b"
+
+client = openai.AsyncOpenAI(api_key=settings.cerebras_api_key, base_url=settings.cerebras_base_url)
+
+model = OpenAIModel(
+    model_name=model_name,
+    provider=OpenAIProvider(openai_client=client),
+)
+
+@dataclass
+class Deps:
+    pass
+
+class Form(BaseModel):
+    customername: str = Field(description="The name of the customer making the request if given, else 'unknown'")
+    requesttype: str = Field(description="The type of request being made. example: 'technical support', 'billing', etc.")
+    issue: str = Field(description="Detailed description of 50 lines of the issue being reported by the customer")
+    emotion: str = Field(description="The emotion of the customer to be given in one word. example: 'angry', 'happy', 'sad', etc.")
+
+with open("form_prompt.txt", "r") as file:
+    form_prompt = file.read()
+
+with open("response_prompt.txt", "r") as file:
+    response_prompt = file.read()
+
+form_agent = Agent(
+    model=model,
+    model_settings=groq_settings,
+    system_prompt=form_prompt,
+    retries=3,
+    result_type=Form,
+)
+
+response_agent = Agent(
+    model=model,
+    model_settings=groq_settings,
+    system_prompt=response_prompt,
+    retries=3,
+)
+
+# Code below is only for debugging please ignore
+
+async def chat():
+    while True:
+        user_message = input("You: ")
+
+        if user_message == "exit":
+            break
+
+        result = await form_agent.run(user_prompt=user_message)
+        response = result.data if result else "Sorry, I failed to process that."
+
+        print("Bot:", response)
+
+# asyncio.run(chat())

app.py

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+import gradio as gr
+import pandas as pd
+import os
+import atexit
+from fastrtc import WebRTC, ReplyOnPause, get_stt_model, get_tts_model
+from settings import Settings
+from pydantic_ai.messages import (
+    ModelMessage,
+    ModelRequest,
+    ModelResponse,
+    UserPromptPart,
+    TextPart
+)
+from agents import form_agent, response_agent
+
+# Config and Globals
+settings = Settings()
+stt_model = get_stt_model()
+tts_model = get_tts_model()
+messages: list[ModelMessage] = []
+
+DATA_PATH = "data.csv"
+df = pd.read_csv(DATA_PATH) if os.path.exists(DATA_PATH) else pd.DataFrame(columns=["customer_name", "request_type", "issue", "emotion"])
+
+def save_data_on_exit():
+    df.to_csv(DATA_PATH, index=False)
+
+atexit.register(save_data_on_exit)
+
+def df_update():
+    global df
+    try:
+        form_response = form_agent.run_sync(user_prompt="Do your thing", message_history=messages)
+        new_row = {
+            "customer_name": form_response.data.customername,
+            "request_type": form_response.data.requesttype,
+            "issue": form_response.data.issue,
+            "emotion": form_response.data.emotion
+        }
+        df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
+        df.to_csv(DATA_PATH, index=False)
+        return "✅ DataFrame updated successfully!"
+    except Exception as e:
+        return f"❌ Update failed: {str(e)}"
+
+def update_table():
+    global df
+    if os.path.exists(DATA_PATH):
+        df = pd.read_csv(DATA_PATH)
+    else:
+        df = pd.DataFrame(columns=["customer_name", "request_type", "issue", "emotion"])
+    return df
+
+def reset_memory():
+    global messages
+    messages = []
+    return "🧠 Memory reset successfully."
+
+async def handle_audio(audio):
+    prompt = stt_model.stt(audio)
+    response_text = await response_agent.run(user_prompt=prompt, message_history=messages)
+    messages.append(ModelRequest(parts=[UserPromptPart(content=prompt)]))
+    messages.append(ModelResponse(parts=[TextPart(content=response_text.data)]))
+    for chunk in tts_model.stream_tts(response_text.data):
+        yield chunk
+
+async def handle_text_chat(user_text, history):
+    response = await response_agent.run(user_prompt=user_text, message_history=messages)
+    messages.append(ModelRequest(parts=[UserPromptPart(content=user_text)]))
+    messages.append(ModelResponse(parts=[TextPart(content=response.data)]))
+    history = history + [[user_text, response.data]]
+    return "", history
+
+# Gradio UI
+with gr.Blocks(css="""
+.toolbox { display: flex; gap: 0.5rem; margin-top: 0.5rem; }
+.footer-note { text-align: center; font-size: 0.85rem; color: #666; margin-top: 1rem; }
+""") as demo:
+    gr.Markdown("<h2 style='text-align: center;'>💬 Customer Support Assistant</h2>")
+
+    debug_box = gr.Textbox(visible=False)
+
+    with gr.Tabs():
+        with gr.Tab("Chat"):
+            with gr.Row():
+                with gr.Column(scale=3):
+                    chatbot = gr.Chatbot(label="Chat Interface")
+                    user_input = gr.Textbox(placeholder="Type your message...", show_label=False)
+                    user_input.submit(fn=handle_text_chat, inputs=[user_input, chatbot], outputs=[user_input, chatbot])
+
+                with gr.Column(scale=1):
+                    mic_button = WebRTC(mode="send-receive", modality="audio")
+                    mic_button.stream(fn=ReplyOnPause(handle_audio), inputs=[mic_button], outputs=[mic_button], time_limit=60)
+
+        with gr.Tab("Customer Data"):
+            gr.Markdown("### Customer Information Table")
+            data_frame = gr.Dataframe(
+                headers=["customer_name", "request_type", "issue", "emotion"],
+                interactive=False,
+                wrap=True
+            )
+            with gr.Row(elem_classes="toolbox"):
+                update_button = gr.Button("📤 Update DataFrame")
+                refresh_button = gr.Button("🔄 Refresh Table")
+                reset_button = gr.Button("🪹 Reset Memory")
+
+            update_button.click(fn=df_update, outputs=[debug_box])
+            refresh_button.click(fn=update_table, outputs=[data_frame])
+            reset_button.click(fn=reset_memory, outputs=[debug_box])
+
+    # Toast feedback
+    def show_toast(msg: str):
+        if msg:
+            gr.Info(msg)
+
+    debug_box.change(fn=show_toast, inputs=[debug_box])
+
+    # Footer
+    gr.Markdown("<div class='footer-note'>🚀 Made with ❤️ by Rikhil</div>")
+
+    demo.load(fn=update_table, outputs=[data_frame])
+
+
+if __name__ == "__main__":
+    demo.launch()

data.csv

@@ -0,0 +1,5 @@
+customer_name,request_type,issue,emotion
+Rikhil,technical support,"The customer, Rikhil, is experiencing issues with the quality of their TV and the installation team has not been responsive.",devastated
+unknown,unknown,The customer did not provide any information about their issue.,unknown
+Sanjana,Installation Support,The customer needs help with the installation of her fridge.,helpless
+Nishant,Technical Support,The suction power of the vacuum cleaner has decreased. The customer has tried troubleshooting but the issue persists. A technician is required to check and repair the device.,Frustrated

form_prompt.txt

@@ -0,0 +1,2 @@
+You are an AI chatbot tasked with providing extra support to a human operator who is receiving a customer support request.
+You will be provided with a transcript of the conversation.

main.py

@@ -0,0 +1,30 @@
+from typing import List
+from fastrtc import (ReplyOnPause, Stream, get_stt_model, get_tts_model)
+from agent import agent
+from pydantic_ai.messages import ModelMessage, ModelRequest, ModelResponse, UserPromptPart, TextPart
+from settings import Settings
+
+settings = Settings()
+hf_token = settings.hf_token
+stt_model = get_stt_model()
+tts_model = get_tts_model()
+
+messages: List[ModelMessage] = []
+
+def echo(audio):
+    prompt = stt_model.stt(audio)
+    
+    response = agent.run_sync(user_prompt=prompt, message_history=messages)
+
+    messages.append(ModelRequest(parts=[UserPromptPart(content=prompt)]))
+    messages.append(ModelResponse(parts=[TextPart(content=response.data)]))
+
+    for audio_chunk in tts_model.stream_tts_sync(response.data):
+        yield audio_chunk
+
+stream = Stream(
+    handler=ReplyOnPause(echo),
+    modality="audio", 
+    mode="send-receive")
+
+stream.ui.launch()

pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "CustomerSupportAgent"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "fastrtc[stt,tts,vad]>=0.0.19",
+    "gradio>=5.23.3",
+    "openai>=1.70.0",
+    "pandas>=2.2.3",
+    "pydantic>=2.11.2",
+    "pydantic-ai>=0.0.52",
+    "pydantic-settings>=2.8.1",
+]

response_prompt.txt

@@ -0,0 +1 @@
+You are a customer support bot and are to be very courteous to the customers who come with complaints, never claim it is out of your scope always guide them

settings.py

@@ -0,0 +1,11 @@
+from pydantic_settings import BaseSettings
+from pydantic import Field
+
+class Settings(BaseSettings):
+    
+    cerebras_api_key : str = Field(..., validate_alias="CEREBRAS_API_KEY")
+    cerebras_base_url : str = Field(..., validate_alias="CEREBRAS_BASE_URL")
+    
+    class Config:
+        env_file = ".env"
+