Create app.py

app.py

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+from flask import Flask, render_template, request, jsonify
+from huggingface_hub import InferenceClient
+
+# Initialize the Flask app
+app = Flask(__name__)
+
+# Initialize the Hugging Face Inference Client (Replace with your actual model identifier)
+client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
+
+# Parameters from the image
+MAX_TOKENS = 1520
+TEMPERATURE = 0.7
+TOP_P = 0.95
+
+# In-memory storage for active chats (to maintain chat history)
+chat_history = {}
+
+@app.route("/")
+def home():
+    return render_template("editor.html")
+
+@app.route("/generate_code", methods=["POST"])
+def generate_code():
+    # Get the user ID (or session) and the prompt
+    user_id = request.json.get("user_id")
+    prompt = request.json.get("prompt")
+
+    # Get chat history for the user or initialize it
+    if user_id not in chat_history:
+        chat_history[user_id] = []
+
+    # Append the user's prompt to the chat history
+    chat_history[user_id].append({"role": "user", "content": prompt})
+
+    # System message
+    system_message = "You are a friendly chatbot."
+
+    # Build the messages for the model
+    messages = [{"role": "system", "content": system_message}]
+    messages.extend(chat_history[user_id])  # Add previous conversation history
+
+    # Generate the response
+    generated_code = ""
+    for msg in client.chat_completion(
+        messages=messages,
+        max_tokens=MAX_TOKENS,
+        temperature=TEMPERATURE,
+        top_p=TOP_P,
+        stream=True,
+    ):
+        token = msg.choices[0].delta.content
+        generated_code += token
+
+    # Save the assistant's response to the chat history
+    chat_history[user_id].append({"role": "assistant", "content": generated_code})
+
+    return jsonify({"code": generated_code})
+
+if __name__ == "__main__":
+    app.run(debug=True, port=7860)
+