Upload main.py

main.py

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+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)