Update main.py

main.py

@@ -1,110 +1,154 @@
+# import gradio as gr
+# from huggingface_hub import InferenceClient
+# import random
+
+# models = [
+#     "google/gemma-7b",
+#     "google/gemma-7b-it",
+#     "google/gemma-2b",
+#     "google/gemma-2b-it"
+# ]
+
+# clients = []
+# for model in models:
+#     clients.append(InferenceClient(model))
+
+
+# def format_prompt(message, history):
+#     prompt = ""
+#     if history:
+#         for user_prompt, bot_response in history:
+#             prompt += f"<start_of_turn>user{user_prompt}<end_of_turn>"
+#             prompt += f"<start_of_turn>model{bot_response}"
+#     prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model"
+#     return prompt
+
+
+# def chat_inf(system_prompt, prompt, history, client_choice, seed, temp, tokens, top_p, rep_p):
+#     client = clients[int(client_choice) - 1]
+#     if not history:
+#         history = []
+#         hist_len = 0
+#     if history:
+#         hist_len = len(history)
+#         print(hist_len)
+
+#     generate_kwargs = dict(
+#         temperature=temp,
+#         max_new_tokens=tokens,
+#         top_p=top_p,
+#         repetition_penalty=rep_p,
+#         do_sample=True,
+#         seed=seed,
+#     )
+#     formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
+#                                     return_full_text=False)
+#     output = ""
+
+#     for response in stream:
+#         output += response.token.text
+#         yield [(prompt, output)]
+#     history.append((prompt, output))
+#     yield history
+
+
+# def clear_fn():
+#     return None
+
+
+# rand_val = random.randint(1, 1111111111111111)
+
+
+# def check_rand(inp, val):
+#     if inp is True:
+#         return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
+#     else:
+#         return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
+
+
+# with gr.Blocks() as app:
+#     gr.HTML(
+#         """<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1></center>""")
+#     with gr.Group():
+#         with gr.Row():
+#             client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0],
+#                                         interactive=True)
+#     chat_b = gr.Chatbot(height=500)
+#     with gr.Group():
+#         with gr.Row():
+#             with gr.Column(scale=1):
+#                 with gr.Group():
+#                     rand = gr.Checkbox(label="Random Seed", value=True)
+#                     seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
+#                     tokens = gr.Slider(label="Max new tokens", value=6400, minimum=0, maximum=8000, step=64,
+#                                        interactive=True, visible=True, info="The maximum number of tokens")
+#             with gr.Column(scale=1):
+#                 with gr.Group():
+#                     temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+#                     top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+#                     rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
+
+#     with gr.Group():
+#         with gr.Row():
+#             with gr.Column(scale=3):
+#                 sys_inp = gr.Textbox(label="System Prompt (optional)")
+#                 inp = gr.Textbox(label="Prompt")
+#                 with gr.Row():
+#                     btn = gr.Button("Chat")
+#                     stop_btn = gr.Button("Stop")
+#                     clear_btn = gr.Button("Clear")
+
+#     chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_inf,
+#                                                                [sys_inp, inp, chat_b, client_choice, seed, temp, tokens,
+#                                                                 top_p, rep_p], chat_b)
+#     go = btn.click(check_rand, [rand, seed], seed).then(chat_inf,
+#                                                         [sys_inp, inp, chat_b, client_choice, seed, temp, tokens, top_p,
+#                                                          rep_p], chat_b)
+#     stop_btn.click(None, None, None, cancels=[go, chat_sub])
+#     clear_btn.click(clear_fn, None, [chat_b])
+# app.queue(default_concurrency_limit=10).launch()
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-import random
-
-models = [
-    "google/gemma-7b",
-    "google/gemma-7b-it",
-    "google/gemma-2b",
-    "google/gemma-2b-it"
-]
-
-clients = []
-for model in models:
-    clients.append(InferenceClient(model))
-
-
-def format_prompt(message, history):
-    prompt = ""
-    if history:
-        for user_prompt, bot_response in history:
-            prompt += f"<start_of_turn>user{user_prompt}<end_of_turn>"
-            prompt += f"<start_of_turn>model{bot_response}"
-    prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model"
-    return prompt
-
-
-def chat_inf(system_prompt, prompt, history, client_choice, seed, temp, tokens, top_p, rep_p):
-    client = clients[int(client_choice) - 1]
-    if not history:
-        history = []
-        hist_len = 0
-    if history:
-        hist_len = len(history)
-        print(hist_len)
-
-    generate_kwargs = dict(
-        temperature=temp,
-        max_new_tokens=tokens,
-        top_p=top_p,
-        repetition_penalty=rep_p,
-        do_sample=True,
-        seed=seed,
-    )
-    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
-                                    return_full_text=False)
-    output = ""
-
-    for response in stream:
-        output += response.token.text
-        yield [(prompt, output)]
-    history.append((prompt, output))
-    yield history
-
-
-def clear_fn():
-    return None
-
-
-rand_val = random.randint(1, 1111111111111111)
-
-
-def check_rand(inp, val):
-    if inp is True:
-        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
-    else:
-        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
-
-
-with gr.Blocks() as app:
-    gr.HTML(
-        """<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1></center>""")
-    with gr.Group():
-        with gr.Row():
-            client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0],
-                                        interactive=True)
-    chat_b = gr.Chatbot(height=500)
-    with gr.Group():
-        with gr.Row():
-            with gr.Column(scale=1):
-                with gr.Group():
-                    rand = gr.Checkbox(label="Random Seed", value=True)
-                    seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
-                    tokens = gr.Slider(label="Max new tokens", value=6400, minimum=0, maximum=8000, step=64,
-                                       interactive=True, visible=True, info="The maximum number of tokens")
-            with gr.Column(scale=1):
-                with gr.Group():
-                    temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
-                    top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
-                    rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
-
-    with gr.Group():
-        with gr.Row():
-            with gr.Column(scale=3):
-                sys_inp = gr.Textbox(label="System Prompt (optional)")
-                inp = gr.Textbox(label="Prompt")
-                with gr.Row():
-                    btn = gr.Button("Chat")
-                    stop_btn = gr.Button("Stop")
-                    clear_btn = gr.Button("Clear")
-
-    chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_inf,
-                                                               [sys_inp, inp, chat_b, client_choice, seed, temp, tokens,
-                                                                top_p, rep_p], chat_b)
-    go = btn.click(check_rand, [rand, seed], seed).then(chat_inf,
-                                                        [sys_inp, inp, chat_b, client_choice, seed, temp, tokens, top_p,
-                                                         rep_p], chat_b)
-    stop_btn.click(None, None, None, cancels=[go, chat_sub])
-    clear_btn.click(clear_fn, None, [chat_b])
-app.queue(default_concurrency_limit=10).launch()
+import pandas as pd
+import spacy
+import re
+import time
+import plotly.express as px
+
+# Load spaCy model
+nlp = spacy.load("en_core_web_sm")
+
+class JobPosting:
+    def __init__(self, description):
+        self.description = description
+
+    def extract(self):
+        text = nlp(self.description)
+        
+        # Extract key nouns as qualifications
+        qualifications = [token.text for token in text if token.pos_ == "NOUN"]
+
+        # Extract salary using regex
+        salary_regex = r"\$\d{1,3}K"
+        salary = re.findall(salary_regex, self.description)
+
+        # Extract pre-trained ORG entities as companies
+        orgs = [ent.text for ent in text.ents if ent.label_ == "ORG"]
+
+        # Define responsibilities extraction logic (replace with actual logic)
+        responsibilities = "Define responsibilities here"
+
+        return qualifications, salary, responsibilities
+
+def main(description):
+    job = JobPosting(description)
+    qualifications, salary, responsibilities = job.extract()
+    return f"Qualifications: {qualifications}\nSalary: {salary}\nResponsibilities: {responsibilities}"
+
+iface = gr.Interface(fn=main, 
+                      inputs="text",
+                      outputs="text",
+                      title="Job Posting Extractor",
+                      description="Enter a job description to extract qualifications, salary, and responsibilities.")
+iface.launch()