Rename app.py to application.py

app.py → application.py

@@ -1,101 +1,101 @@
-import os
-from flask import Flask, request, jsonify, render_template, send_from_directory
-from huggingface_hub import InferenceClient
-from datasets import load_dataset
-import markdown2
-import signal
-
-os.environ["HF_HOME"] = "/app/.cache"
-
-application = Flask(__name__, static_folder='static', template_folder='templates')
-
-hf_token = os.getenv("HF_TOKEN")
-
-chat_doctor_dataset = load_dataset("avaliev/chat_doctor")
-mental_health_dataset = load_dataset("Amod/mental_health_counseling_conversations")
-
-client = InferenceClient(
-    "meta-llama/Meta-Llama-3-8B-Instruct",
-    token=hf_token,
-)
-
-def select_relevant_context(user_input):
-    mental_health_keywords = [
-        "anxious", "depressed", "stress", "mental health", "counseling", 
-        "therapy", "feelings", "worthless", "suicidal", "panic", "anxiety"
-    ]
-    medical_keywords = [
-        "symptoms", "diagnosis", "treatment", "doctor", "prescription", "medication",
-        "pain", "illness", "disease", "infection", "surgery"
-    ]
-
-    # Check if the input contains any mental health-related keywords
-    if any(keyword in user_input.lower() for keyword in mental_health_keywords):
-        example = mental_health_dataset['train'][0]
-        context = f"Counselor: {example['Response']}\nUser: {example['Context']}"
-    # Check if the input contains any medical-related keywords
-    elif any(keyword in user_input.lower() for keyword in medical_keywords):
-        example = chat_doctor_dataset['train'][0]
-        context = f"Doctor: {example['input']}\nPatient: {example['output']}"
-    else:
-        # If no specific keywords are found, provide a general response
-        context = "You are a general assistant. Respond to the user's query in a helpful manner."
-
-    return context
-
-def create_prompt(context, user_input):
-    prompt = (
-        f"{context}\n\n"
-        f"User: {user_input}\nAssistant:"
-    )
-    return prompt
-
-# Function to render Markdown into HTML
-def render_markdown(text):
-    return markdown2.markdown(text)
-
-@application.route('/')
-def index():
-    return render_template('index.html')
-
-@application.route('/static/<path:path>')
-def send_static(path):
-    return send_from_directory('static', path)
-
-@application.route('/chat', methods=['POST'])
-def chat():
-    user_input = request.json['message']
-    
-    context = select_relevant_context(user_input)
-    
-    prompt = create_prompt(context, user_input)
-    
-    response = ""
-    for message in client.chat_completion(
-        messages=[{"role": "user", "content": prompt}],
-        max_tokens=500,
-        stream=True,
-    ):
-        response += message.choices[0].delta.content
-    
-    formatted_response = render_markdown(response)
-    
-    return jsonify({"response": formatted_response})
-
-@application.route('/shutdown', methods=['POST'])
-def shutdown():
-    if request.environ.get('werkzeug.server.shutdown'):
-        shutdown_server()
-    else:
-        os.kill(os.getpid(), signal.SIGINT)
-    return jsonify({"message": "Server is shutting down..."})
-
-def shutdown_server():
-    func = request.environ.get('werkzeug.server.shutdown')
-    if func is None:
-        os.kill(os.getpid(), signal.SIGINT)  # Kill the process if Werkzeug is not available
-    else:
-        func()
-
-if __name__ == '__main__':
-    application.run(host='0.0.0.0', port=8080)
+import os
+from flask import Flask, request, jsonify, render_template, send_from_directory
+from huggingface_hub import InferenceClient
+from datasets import load_dataset
+import markdown2
+import signal
+
+os.environ["HF_HOME"] = "/app/.cache"
+
+application = Flask(__name__, static_folder='static', template_folder='templates')
+
+hf_token = os.getenv("HF_TOKEN")
+
+chat_doctor_dataset = load_dataset("avaliev/chat_doctor")
+mental_health_dataset = load_dataset("Amod/mental_health_counseling_conversations")
+
+client = InferenceClient(
+    "meta-llama/Meta-Llama-3-8B-Instruct",
+    token=hf_token,
+)
+
+def select_relevant_context(user_input):
+    mental_health_keywords = [
+        "anxious", "depressed", "stress", "mental health", "counseling", 
+        "therapy", "feelings", "worthless", "suicidal", "panic", "anxiety"
+    ]
+    medical_keywords = [
+        "symptoms", "diagnosis", "treatment", "doctor", "prescription", "medication",
+        "pain", "illness", "disease", "infection", "surgery"
+    ]
+
+    # Check if the input contains any mental health-related keywords
+    if any(keyword in user_input.lower() for keyword in mental_health_keywords):
+        example = mental_health_dataset['train'][0]
+        context = f"Counselor: {example['Response']}\nUser: {example['Context']}"
+    # Check if the input contains any medical-related keywords
+    elif any(keyword in user_input.lower() for keyword in medical_keywords):
+        example = chat_doctor_dataset['train'][0]
+        context = f"Doctor: {example['input']}\nPatient: {example['output']}"
+    else:
+        # If no specific keywords are found, provide a general response
+        context = "You are a general assistant. Respond to the user's query in a helpful manner."
+
+    return context
+
+def create_prompt(context, user_input):
+    prompt = (
+        f"{context}\n\n"
+        f"User: {user_input}\nAssistant:"
+    )
+    return prompt
+
+# Function to render Markdown into HTML
+def render_markdown(text):
+    return markdown2.markdown(text)
+
+@application.route('/')
+def index():
+    return render_template('index.html')
+
+@application.route('/static/<path:path>')
+def send_static(path):
+    return send_from_directory('static', path)
+
+@application.route('/chat', methods=['POST'])
+def chat():
+    user_input = request.json['message']
+    
+    context = select_relevant_context(user_input)
+    
+    prompt = create_prompt(context, user_input)
+    
+    response = ""
+    for message in client.chat_completion(
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=500,
+        stream=True,
+    ):
+        response += message.choices[0].delta.content
+    
+    formatted_response = render_markdown(response)
+    
+    return jsonify({"response": formatted_response})
+
+@application.route('/shutdown', methods=['POST'])
+def shutdown():
+    if request.environ.get('werkzeug.server.shutdown'):
+        shutdown_server()
+    else:
+        os.kill(os.getpid(), signal.SIGINT)
+    return jsonify({"message": "Server is shutting down..."})
+
+def shutdown_server():
+    func = request.environ.get('werkzeug.server.shutdown')
+    if func is None:
+        os.kill(os.getpid(), signal.SIGINT)  # Kill the process if Werkzeug is not available
+    else:
+        func()
+
+if __name__ == '__main__':
+    application.run(host='0.0.0.0', port=7680)