Update app.py

app.py

@@ -1,11 +1,39 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import PyPDF2
+import docx
+import io
 
-"""
-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 extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file))
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text() + "\n"
+    return text
+
+def extract_text_from_docx(docx_file):
+    doc = docx.Document(io.BytesIO(docx_file))
+    return "\n".join([para.text for para in doc.paragraphs])
+
+def parse_cv(file):
+    if file is None:
+        return "Please upload a CV file."
+    
+    file_ext = file.name.split(".")[-1].lower()
+    file_bytes = file.read()
+    
+    if file_ext == "pdf":
+        text = extract_text_from_pdf(file_bytes)
+    elif file_ext == "docx":
+        text = extract_text_from_docx(file_bytes)
+    else:
+        return "Unsupported file format. Please upload a PDF or DOCX file."
+    
+    prompt = f"Analyze the following CV and generate a professional summary and improvement suggestions:\n\n{text}"
+    response = client.text_generation(prompt, max_tokens=512)
+    return response
 
 def respond(
     message,
@@ -24,7 +52,6 @@ def respond(
             messages.append({"role": "assistant", "content": val[1]})
 
     messages.append({"role": "user", "content": message})
-
     response = ""
 
     for message in client.chat_completion(
@@ -35,30 +62,37 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
         yield response
 
+demo = gr.Blocks()
 
-"""
-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)",
-        ),
-    ],
-)
-
+with demo:
+    gr.Markdown("## AI-powered CV Analyzer and Chatbot")
+    with gr.Tab("Chatbot"):
+        chat_interface = 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)",
+                ),
+            ],
+        )
+    
+    with gr.Tab("CV Analyzer"):
+        gr.Markdown("### Upload your CV (PDF or DOCX) to receive a professional analysis.")
+        file_input = gr.File(label="Upload CV", type="file")
+        output_text = gr.Textbox(label="CV Analysis Report", lines=10)
+        analyze_button = gr.Button("Analyze CV")
+        
+        analyze_button.click(parse_cv, inputs=file_input, outputs=output_text)
 
 if __name__ == "__main__":
     demo.launch()