update nltk

main.py

@@ -1,90 +1,96 @@
-from flask import Flask, request, jsonify
-import os
-import nltk
-from langchain.llms import LlamaCpp
-from langchain.callbacks.manager import CallbackManager
-from langchain.prompts import PromptTemplate
-from langchain.schema.output_parser import StrOutputParser
-
-nltk.download('punkt')
-
-app = Flask(__name__)
-
-# Download model
-if not os.path.exists('phi-2.Q4_K_M.gguf'):
-    os.system('wget https://huggingface.co/TheBloke/phi-2-GGUF/resolve/main/phi-2.Q4_K_M.gguf')
-
-# Disable GPU usage
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-
-# Callback manager setup
-callback_manager = CallbackManager([])
-
-# Creating LlamaCpp instance
-llm = LlamaCpp(
-    model_path="phi-2.Q4_K_M.gguf",
-    temperature=0.1,
-    n_gpu_layers=0,
-    n_batch=1024,
-    callback_manager=callback_manager,
-    verbose=True,
-    n_ctx=2048
-)
-
-# Define templates
-templates = {
-    "work_experience": """Instruction:
-    Extract and summarize the work experience mentioned in the CV provided below. Focus solely on the details related to work history, including job titles, companies, and duration.
-    Text: {text}
-    Question: {question}
-    Output:""",
-    
-    "certification": """Instruction:
-    Extract and summarize the certification history mentioned in the CV provided below. Include details such as degrees earned, institutions attended, and graduation years.
-    Text: {text}
-    Question: {question}
-    Output:""",
-    
-    "contact_info": """Instruction:
-    Extract and provide the contact information mentioned in the CV provided below. Include details such as phone number, email address, and any other relevant contact links.
-    Text: {text}
-    Question: {question}
-    Output:""",
-    
-    "skills": """Instruction:
-    Focus solely on extracting the skills mentioned in the text below, excluding any other details or context. Your answer should consist of concise skills.
-    Text: {text}
-    Question: {question}
-    Output:"""
-}
-
-@app.route('/', methods=['POST'])
-def generate_text():
-    data = request.get_json()
-    question = data.get('question')
-    text = data.get('text')
-
-    if not question or not text:
-        return jsonify({"error": "Both 'question' and 'text' fields are required."}), 400
-
-    if question == "Please summarize the work experience mentioned in the CV.":
-        template_key = "work_experience"
-    elif question == "Please summarize the certification history mentioned in the CV without repeating the output only once.":
-        template_key = "certification"
-    elif question == "Please extract the contact information mentioned in the CV once.":
-        template_key = "contact_info"
-    elif question == "What are the 6 skills? Please provide a concise short answer of the only(skills) mentioned in the text without repeating the answer.":
-        template_key = "skills"
-    else:
-        return jsonify({"error": "Invalid question provided."}), 400
-
-    prompt = PromptTemplate(template=templates[template_key], input_variables=["question", "text"])
-    chain = prompt | llm | StrOutputParser()
-    response = chain.invoke({"question": question, "text": text})
-
-    return jsonify({"generated_text": response})
-
-
-if __name__ == '__main__':
-    port = int(os.environ.get("PORT", 8000))
-    app.run( port= 8000)
+from flask import Flask, request, jsonify
+import os
+import nltk
+from langchain.llms import LlamaCpp
+from langchain.callbacks.manager import CallbackManager
+from langchain.prompts import PromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+
+nltk_data_dir = "./nltk_data_dir/"
+if not os.path.exists(nltk_data_dir):
+    os.makedirs(nltk_data_dir, exist_ok=True)
+nltk.data.path.clear()
+nltk.data.path.append(nltk_data_dir)
+
+nltk.download('punkt',download_dir=nltk_data_dir)
+
+app = Flask(__name__)
+
+# Download model
+if not os.path.exists('phi-2.Q4_K_M.gguf'):
+    os.system('wget https://huggingface.co/TheBloke/phi-2-GGUF/resolve/main/phi-2.Q4_K_M.gguf')
+
+# Disable GPU usage
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+# Callback manager setup
+callback_manager = CallbackManager([])
+
+# Creating LlamaCpp instance
+llm = LlamaCpp(
+    model_path="phi-2.Q4_K_M.gguf",
+    temperature=0.1,
+    n_gpu_layers=0,
+    n_batch=1024,
+    callback_manager=callback_manager,
+    verbose=True,
+    n_ctx=2048
+)
+
+# Define templates
+templates = {
+    "work_experience": """Instruction:
+    Extract and summarize the work experience mentioned in the CV provided below. Focus solely on the details related to work history, including job titles, companies, and duration.
+    Text: {text}
+    Question: {question}
+    Output:""",
+    
+    "certification": """Instruction:
+    Extract and summarize the certification history mentioned in the CV provided below. Include details such as degrees earned, institutions attended, and graduation years.
+    Text: {text}
+    Question: {question}
+    Output:""",
+    
+    "contact_info": """Instruction:
+    Extract and provide the contact information mentioned in the CV provided below. Include details such as phone number, email address, and any other relevant contact links.
+    Text: {text}
+    Question: {question}
+    Output:""",
+    
+    "skills": """Instruction:
+    Focus solely on extracting the skills mentioned in the text below, excluding any other details or context. Your answer should consist of concise skills.
+    Text: {text}
+    Question: {question}
+    Output:"""
+}
+
+@app.route('/', methods=['POST'])
+def generate_text():
+    data = request.get_json()
+    question = data.get('question')
+    text = data.get('text')
+
+    if not question or not text:
+        return jsonify({"error": "Both 'question' and 'text' fields are required."}), 400
+
+    if question == "Please summarize the work experience mentioned in the CV.":
+        template_key = "work_experience"
+    elif question == "Please summarize the certification history mentioned in the CV without repeating the output only once.":
+        template_key = "certification"
+    elif question == "Please extract the contact information mentioned in the CV once.":
+        template_key = "contact_info"
+    elif question == "What are the 6 skills? Please provide a concise short answer of the only(skills) mentioned in the text without repeating the answer.":
+        template_key = "skills"
+    else:
+        return jsonify({"error": "Invalid question provided."}), 400
+
+    prompt = PromptTemplate(template=templates[template_key], input_variables=["question", "text"])
+    chain = prompt | llm | StrOutputParser()
+    response = chain.invoke({"question": question, "text": text})
+
+    return jsonify({"generated_text": response})
+
+
+if __name__ == '__main__':
+    port = int(os.environ.get("PORT", 8000))
+    app.run( port= 8000)