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.gitattributes

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.github/workflows/check_file_size.yml

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+name: Check file size
+on: # or directly `on: [push]` to run the action on every push on any branch
+  pull_request:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check large files
+        uses: ActionsDesk/[email protected]
+        with:
+          filesizelimit: 10485760

.github/workflows/huggingface.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push -f https://HarshanaLF:[email protected]/spaces/HarshanaLF/Real-Time-Chat-with-AI main

README.md

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----
-title: Chat Flash Sel Model
-emoji: 📉
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.5.0
-app_file: app.py
-pinned: false
-short_description: we can select model and get speedy results
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Real Time Chat With AI
+emoji: ⚡
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.31.5
+app_file: app.py
+pinned: false
+short_description: Chat with AI with ⚡Lightning Speed
+---
+
+# H GO
+
+Inspired by Google Go, H GO is a concise and efficient chat interface that leverages various language models hosted on Hugging Face. It uses Gradio for the user interface and supports different models to cater to diverse needs.
+
+## Features
+
+- **Multiple Model Support**: Choose from various models such as Nous Hermes, StarChat, Mistral, and Phi.
+- **Real-time Interaction**: Get quick and concise responses from the selected model.
+- **Customizable**: Easily switch models to suit your specific requirements.
+
+## Setup
+
+### Prerequisites
+
+- Python 3.7+
+- Gradio
+- Hugging Face Hub
+- Git LFS (Large File Storage)
+
+### Installation
+
+1. Clone the repository:
+
+   ```bash
+   # Make sure you have git-lfs installed (https://git-lfs.com)
+   git lfs install
+
+   # When prompted for a password, use an access token with write permissions.
+   # Generate one from your settings: https://huggingface.co/settings/tokens
+   git clone https://huggingface.co/spaces/HarshanaLF/Real-Time-Chat-with-AI
+
+   # If you want to clone without large files - just their pointers
+   GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/spaces/HarshanaLF/Real-Time-Chat-with-AI
+   ```
+
+2. Navigate to the project directory:
+
+   ```bash
+   cd Real-Time-Chat-with-AI
+   ```
+
+3. Install the required dependencies:
+
+   ```bash
+   pip install gradio huggingface_hub
+   ```
+
+## Usage
+
+Run the application using the following command:
+
+```bash
+python app.py
+```
+
+## Model Descriptions
+
+- **Nous Hermes Mixtral 8x7B DPO**: A robust model designed for detailed and nuanced conversation handling.
+- **StarChat2 15b**: A large-scale model optimized for general chat interactions with a wide range of topics.
+- **Mistral 7B v0.3**: A smaller, efficient model suitable for fast and responsive chat applications.
+- **Phi 3 mini**: A compact model focusing on instructive and concise responses.
+- **Mixtral 8x7B**: A versatile model capable of handling various conversational contexts effectively.
+
+```
+
+```

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Function to return the appropriate client based on the model selected
+def client_fn(model):
+    model_map = {
+        "Nous Hermes Mixtral 8x7B DPO": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
+        "StarChat2 15b": "HuggingFaceH4/starchat2-15b-v0.1",
+        "Mistral 7B v0.3": "mistralai/Mistral-7B-Instruct-v0.3",
+        "Phi 3 mini": "microsoft/Phi-3-mini-4k-instruct",
+        "Mixtral 8x7B": "mistralai/Mixtral-8x7B-Instruct-v0.1"
+    }
+    return InferenceClient(model_map.get(model, "mistralai/Mixtral-8x7B-Instruct-v0.1"))
+
+system_instructions = ("[SYSTEM] You are a chat bot named 'H go'."
+                       "Your task is to Answer the question."
+                       "Keep conversation very short, clear and concise."
+                       "Respond naturally and concisely to the user's queries. "
+                       "The expectation is that you will avoid introductions and start answering the query directly, Only answer the question asked by user, Do not say unnecessary things."
+                       "Begin with a greeting if the user initiates the conversation. "
+                       "Here is the user's query:[QUESTION] ")
+
+# Function to generate model responses
+def models(text, model="Mixtral 8x7B"):
+    client = client_fn(model)
+    generate_kwargs = {
+        "max_new_tokens": 100,
+        "do_sample": True,
+    }
+    
+    formatted_prompt = f"{system_instructions} {text} [ANSWER]"
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    
+    output = ""
+    for response in stream:
+        output += response.token.text
+        if output.endswith("</s>"):
+            output = output[:-4]
+    return output
+
+# Gradio interface description and configuration
+description = """# H GO
+### Inspired from Google Go"""
+
+with gr.Blocks() as demo:
+    gr.Markdown(description)
+    
+    text_input = gr.Textbox(label="Enter your message here:")
+    dropdown = gr.Dropdown(['Mixtral 8x7B', 'Nous Hermes Mixtral 8x7B DPO', 'StarChat2 15b', 'Mistral 7B v0.3', 'Phi 3 mini'], value="Mistral 7B v0.3", label="Select Model")
+    submit_btn = gr.Button("Send")
+    output_text = gr.Textbox(label="Response")
+    
+    submit_btn.click(fn=models, inputs=[text_input, dropdown], outputs=output_text)
+
+# Queue and launch configuration for Gradio
+demo.queue(max_size=300000)
+demo.launch()