Upload 5 files

README.md

@@ -1,12 +1,12 @@
 ---
 title: Skills Generator
-emoji: 💬
+emoji: 🏢
 colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 4.36.1
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.31.0
 app_file: app.py
 pinned: false
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,63 +1,65 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import streamlit as st
+from langchain.llms import GooglePalm
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.chains import SequentialChain
+
+# Function to generate skills and companies based on the job role
+def generate_skills_and_companies(job_role):
+    # Initialize Google's Palm LLM
+    llm = GooglePalm(google_api_key='AIzaSyAA28rGYJnOsGasVGEQ-dJRHXqLNTVEQz4', temperature=0)  # Replace with your Google API Key
+
+    # Chain 1: This chain is for Job role
+    prompt_template_name = PromptTemplate(
+        input_variables=['job_role'],
+        template="""I want to apply for a {job_role} role. Please help me with the desired skills and ensure you are 
+        giving correct skills as it is critical"""
+    )
+
+    skills_chain = LLMChain(llm=llm, prompt=prompt_template_name, output_key="skills")
+
+    # Chain 2: This is for companies
+    prompt_template_items = PromptTemplate(
+        input_variables=['companies'],
+        template="""Suggest some companies to apply for {job_role}. Return it as a comma separated string"""
+    )
+
+    companies_chain = LLMChain(llm=llm, prompt=prompt_template_items, output_key="companies")
+
+    chain = SequentialChain(
+        chains=[skills_chain, companies_chain],
+        input_variables=['job_role'],
+        output_variables=['skills', "companies"]
+    )
+
+    response = chain({'job_role': job_role})
+
+    return response
+
+# Streamlit app
+st.title("Skills and Companies Recommendation App")
+st.subheader("Developed by Mujeeb")
+
+# Sidebar with image and additional options
+st.sidebar.image("odinschool1.jpg", use_column_width=True)
+
+# Main content area
+job_role = st.text_input("Enter the job role:")
+
+if st.button("Generate"):
+    with st.spinner("Generating recommendations..."):
+        output = generate_skills_and_companies(job_role)
+
+    if output:
+        st.subheader("Skills:")
+        skills = output['skills'].split('\n')
+        for skill in skills:
+            skill = skill.strip('* ').strip()
+            st.write(skill)
+
+        st.subheader("Companies:")
+        companies = output['companies'].split('\n')
+        for company in companies:
+            company = company.strip('* ').strip()
+            st.write(company)
+

gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

requirements.txt

@@ -1 +1,2 @@
-huggingface_hub==0.22.2
+streamlit
+langchain==0.1.5