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  1. Dockerfile +11 -0
  2. Requirements.txt +3 -0
  3. app.py +127 -0
Dockerfile ADDED
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+ FROM python:3.9
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+
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+ WORKDIR /Code
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+
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+ COPY ./Requirements.txt /Code/Requirements.txt
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+
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+ RUN pip install --no-cache-dir --upgrade -r /Code/Requirements.txt
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+
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+ COPY . .
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+
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+ CMD ["flask", "run", "--host", "0.0.0.0", "--port", "7860"]
Requirements.txt ADDED
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+ uvicorn
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+ huggingface_hub
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+ flask
app.py ADDED
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+ # **Durgaai Solutions (India First AI Assistant)**
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+ # **A Flask API for generating text using the Mixtral-7B-Instruct-v0.2 model**
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+
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+ import uvicorn
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+ from flask import Flask, request, jsonify, render_template_string
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+ from huggingface_hub import InferenceClient
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+ import logging
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+
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+ # Configure logging
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+ logging.basicConfig(level=logging.INFO)
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+
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+ # Defining Application
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+ app = Flask(__name__)
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+
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+ # Defining Model Used
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+ used_model = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1"
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+
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+ # Defining Prompt
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+ def customize_prompt(message, final_instructions=None):
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+ prompt = ""
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+ if final_instructions:
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+ prompt += f"[INST] {final_instructions} [/INST]"
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+ prompt += f"[INST] {message} [/INST]"
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+ return prompt
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+
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+ # Main Application
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+ def public_model(prompt, instructions, api, temperature=0.90, max_new_tokens=256, top_p=0.95, repetition_penalty=1.2):
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+ global used_model
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+ try:
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+ temperature = float(temperature)
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+ if temperature < 1e-2:
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+ temperature = 1e-2
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+ top_p = float(top_p)
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+
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+ generate_kwargs = dict(
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+ temperature=temperature,
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+ max_new_tokens=max_new_tokens,
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+ top_p=top_p,
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+ repetition_penalty=repetition_penalty,
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+ do_sample=True,
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+ seed=69,
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+ )
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+ final_instructions = instructions
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+ result = customize_prompt(prompt, final_instructions)
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+
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+ head = {"Authorization": f"Bearer {api}"}
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+ client = InferenceClient(used_model, headers=head)
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+ response = client.text_generation(result, **generate_kwargs)
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+ return response
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+ except Exception as e:
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+ logging.error(f"Error generating text: {e}")
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+ return str(e)
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+
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+ # Running Application
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+ @app.route("/run-application", methods=["POST"])
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+ def run_application():
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+ data = request.json
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+ prompt = data.get("prompt")
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+ instructions = data.get("instructions")
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+ api_key = data.get("api_key")
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+
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+ if not prompt or not instructions or not api_key:
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+ return jsonify({"Error": "Missing Required Fields"}), 400
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+
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+ try:
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+ # Validate API key
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+ if not api_key.startswith("hf_"):
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+ return jsonify({"Error": "Invalid API key"}), 401
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+
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+ response = public_model(prompt, instructions, api_key)
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+ return jsonify({"Response": response}), 200
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+ except Exception as e:
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+ logging.error(f"Error processing request: {e}")
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+ return jsonify({"Error": "Internal Server Error"}), 500
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+
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+ # Basic HTML Interface
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+ html = '''
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+ <!DOCTYPE html>
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+ <html>
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+ <head>
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+ <title>Mixtral 7b Instruct v0.1 = Public Server For API Usage</title>
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+ <style>
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+ body {
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+ font-family: Arial, sans-serif;
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+ }
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+ .Container {
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+ text-align: center;
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+ max-width: 800px;
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+ margin: 50px auto;
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+ padding: 20px;
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+ background-color: #f9f9f9;
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+ border: 4px solid lawngreen;
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+ box-shadow: 0 0 10px rgba(0, 0, 0, 0.2);
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+ border-radius: 12px;
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+ }
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+ .Container h1 {
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+ font-size: 40px;
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+ color: orange;
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+ }
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+ .Container p {
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+ font-size: 20px;
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+ color: darkred;
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+ }
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+ .Container p0{
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+ font-size: 24px;
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+ color: darkmagenta;
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+ }
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+ </style>
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+ </head>
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+ <body>
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+ <div class="Container">
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+ <h1>Mixtral 7b Instruct v0.1 = Public Server</h1>
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+ <p>Welcome To Durgaai Solutions Organisation ( To Use This Server Follow Given Steps )<br></br>1. Create Your Hugging Face Access Token Like : (hf_**********************************)<br></br>2. Go To Our { Github Organisation | Source Code } Page By Link Given Below<br></br>3. Download The Respository Do Suggested Changes To (App.py) File & Its Ready !!!</p>
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+ <p0><a href="https://github.com/DurgaiSolutions/Mixtral-7b-Instruct-v0.1">Github Link</a> : Now Use It As Free With Unlimited Access To This AI Model</p0>
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+ </div>
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+ </body>
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+ </html>
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+ '''
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+
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+ # Launching Interface
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+ @app.route("/", methods=["GET"])
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+ def index():
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+ return render_template_string(html)
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+
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+ # Launch Application
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+ if __name__ == "__main__":
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+ app.run(debug=True)