Update app.py

app.py

@@ -1,64 +1,103 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+import streamlit as st
+from reportlab.pdfgen import canvas
+from io import BytesIO
+import os
+from groq import Groq
+from transformers import pipeline
 
-"""
-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")
+# ========== API Keys ==========
+GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
 
+# ========== Groq Client ==========
+client = Groq(api_key=GROQ_API_KEY)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# ========== Hugging Face Summarizer (optional) ==========
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# ========== Resume Parser ==========
+def parse_resume(uploaded_file):
+    return uploaded_file.read().decode("utf-8")
 
-    messages.append({"role": "user", "content": message})
+# ========== Groq Prompt Engine ==========
+def query_groq(prompt, model="llama3-8b-8192"):
+    chat_completion = client.chat.completions.create(
+        model=model,
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return chat_completion.choices[0].message.content
 
-    response = ""
+# ========== Resume Tip Generator ==========
+def generate_resume_tips(resume_text, exp_level, salary, grad_year, stream, job_desc=None):
+    if job_desc:
+        prompt = f"""Give resume improvement suggestions for this resume based on this job description:
+        Resume:\n{resume_text}\n\nJob Description:\n{job_desc}"""
+    else:
+        prompt = f"""Provide tips to improve the resume based on experience level: {exp_level}, expected salary: {salary}, graduation year: {grad_year}, graduation stream: {stream}.
+        Resume:\n{resume_text}"""
+    return query_groq(prompt)
 
-    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
+# ========== Cover Letter Generator ==========
+def generate_cover_letter(resume_text, job_title, word_limit):
+    prompt = f"""Create a professional cover letter for the job '{job_title}' based on this resume. Limit it to {word_limit} words.\nResume:\n{resume_text}"""
+    return query_groq(prompt)
 
-        response += token
-        yield response
+def create_pdf(text, filename="cover_letter.pdf"):
+    buffer = BytesIO()
+    c = canvas.Canvas(buffer)
+    text_object = c.beginText(40, 800)
+    for line in text.split('\n'):
+        text_object.textLine(line)
+    c.drawText(text_object)
+    c.save()
+    buffer.seek(0)
+    return buffer
 
+# ========== Roadmap Generator ==========
+def generate_roadmap(job_profile):
+    prompt = f"Generate a module-wise detailed roadmap to become a skilled {job_profile}. Divide it into weeks or phases with topics."
+    roadmap = query_groq(prompt)
+    summarized = summarizer(roadmap, max_length=300, min_length=150, do_sample=False)[0]["summary_text"]
+    return summarized, roadmap
 
-"""
-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)",
-        ),
-    ],
-)
+# ========== UI ==========
+st.set_page_config(page_title="Smart Career Chatbot", layout="wide")
+st.title("🧠 Smart Career Chatbot")
 
+st.sidebar.header("User Inputs")
+uploaded_resume = st.sidebar.file_uploader("Upload Resume (Text format only)", type=["txt"])
+exp_level = st.sidebar.selectbox("Experience Level", ["Fresher", "Junior", "Mid", "Senior"])
+salary = st.sidebar.text_input("Expected Salary (in ₹)", "600000")
+grad_year = st.sidebar.text_input("Graduation Year", "2023")
+grad_stream = st.sidebar.text_input("Graduation Stream", "Computer Science")
 
-if __name__ == "__main__":
-    demo.launch()
+if uploaded_resume:
+    resume_text = parse_resume(uploaded_resume)
+    st.subheader("📄 Uploaded Resume Preview")
+    st.text_area("Resume Content", resume_text, height=300)
+
+    st.subheader("💡 Get Resume Tips")
+    job_desc = st.text_area("Optional: Paste a Job Description for Custom Tips")
+    if st.button("Get Resume Tips"):
+        tips = generate_resume_tips(resume_text, exp_level, salary, grad_year, grad_stream, job_desc)
+        st.success("Resume Tips Generated:")
+        st.write(tips)
+
+    st.subheader("✉️ Generate Cover Letter")
+    job_title = st.text_input("Target Job Title", "Software Engineer")
+    word_limit = st.slider("Word Limit", min_value=100, max_value=600, value=300)
+    if st.button("Generate Cover Letter"):
+        cover_letter = generate_cover_letter(resume_text, job_title, word_limit)
+        st.text_area("Generated Cover Letter", cover_letter, height=300)
+        pdf_file = create_pdf(cover_letter)
+        st.download_button("Download Cover Letter PDF", pdf_file, file_name="cover_letter.pdf")
+
+    st.subheader("🗺️ Generate Career Roadmap")
+    target_profile = st.text_input("Enter Job Profile (e.g., Data Scientist, Full Stack Developer)")
+    if st.button("Generate Roadmap"):
+        summary, full_roadmap = generate_roadmap(target_profile)
+        st.info("📌 Summary:")
+        st.write(summary)
+        with st.expander("🔍 Full Roadmap"):
+            st.write(full_roadmap)
+else:
+    st.warning("Please upload your resume to start.")