Update app.py

app.py

@@ -10,26 +10,16 @@ import subprocess
 from huggingface_hub import hf_hub_download
 from llama_cpp import Llama
 
-model_name = "Meta-Llama-3-70B-Instruct-Q3_K_M.gguf"
+repo_id = "srijaydeshpande/CVCRaft"
+model_id = "cvcraft.gguf"
 
-# hf_hub_download(
-#     repo_id="QuantFactory/Meta-Llama-3-8B-Instruct-GGUF",
-#     filename="Meta-Llama-3-8B-Instruct.Q8_0.gguf",
-#     local_dir = "./models"
-# )
 
 hf_hub_download(
-    repo_id="bartowski/Meta-Llama-3-70B-Instruct-GGUF",
-    filename=model_name,
+    repo_id=repo_id,
+    filename=model_id,
     local_dir = "./models"
 )
 
-# hf_hub_download(
-#     repo_id="leafspark/Meta-Llama-3.1-405B-Instruct-GGUF",
-#     filename="Llama-3.1-405B-Instruct.Q2_K.gguf",
-#     local_dir = "./models"
-# )
-
 def process_document(pdf_path):
     extracted_pages = extract_pages(pdf_path)
     page2content = {}
@@ -77,15 +67,11 @@ def txt_to_html(text):
     html_content += "</body></html>"
     return html_content
 
-def craft_cv(llm, cv_text, prompt, maxtokens, temperature, top_probability):
-    task = 'Please modify the CV text according the following job description: '
+def craft_cv(llm, cv_text, job_description, maxtokens, temperature, top_probability):
+    instruction = "Given input CV and job description. Please revise the CV according to the given job description and output the revised CV."
     output = llm.create_chat_completion(
         messages=[
-            {"role": "assistant", "content": task + prompt},
-            {
-                "role": "user",
-                "content": cv_text
-            }
+            {"from": "user", "value": instruction + ' Input CV: ' + cv_text + ' , Job Description: ' + job_description},
         ],
         max_tokens=maxtokens,
         temperature=temperature
@@ -95,21 +81,20 @@ def craft_cv(llm, cv_text, prompt, maxtokens, temperature, top_probability):
     return cv_text, output
 
 @spaces.GPU(duration=150)
-def pdf_to_text(cv_file, prompt, maxtokens=2048, temperature=0, top_probability=0.95):
+def pdf_to_text(cv_file, job_description, maxtokens=2048, temperature=0, top_probability=0.95):
     page2content = process_document(cv_file)
     cv_text = ""
     for page_id in page2content:
         cv_text += page2content[page_id] + ' '
-    # llm = Llama(
-    #     model_path="models/" + model_name,
-    #     flash_attn=True,
-    #     n_gpu_layers=81,
-    #     n_batch=1024,
-    #     n_ctx=8192,
-    # )
-    # cv_text, crafted_cv = craft_cv(llm, cv_text, prompt, maxtokens, temperature, top_probability)
-    crafted_cv = ''
-    return cv_text, crafted_cv
+    llm = Llama(
+        model_path="models/" + model_id,
+        flash_attn=True,
+        n_gpu_layers=81,
+        n_batch=1024,
+        n_ctx=8192,
+    )
+    cv_text, crafted_cv = craft_cv(llm, cv_text, job_description, maxtokens, temperature, top_probability)
+    return crafted_cv
 
 temp_slider = gr.Slider(minimum=0, maximum=2, value=0.9, label="Temperature Value")
 prob_slider = gr.Slider(minimum=0, maximum=1, value=0.95, label="Max Probability Value")
@@ -120,7 +105,7 @@ output_text = gr.Textbox()
 iface = gr.Interface(
     fn=pdf_to_text,
     inputs=[cv_file, prompt_text],
-    outputs=['text', 'text'],
+    outputs=['text'],
     title='Craft CV',
     description="This application assists to customize CV based on input job description",
     theme=gr.themes.Soft(),