Update app.py

app.py

@@ -2,13 +2,13 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import PyPDF2
 import io
-from docx import Document  # Make sure you have installed python-docx
+from docx import Document  # Ensure you have installed "python-docx" (not "docx")
 
-# Initialize the client for Hugging Face inference.
+# Initialize the inference client from Hugging Face.
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 def extract_text_from_pdf(pdf_file_bytes):
-    """Extract text from a PDF file given as bytes."""
+    """Extract text from PDF bytes."""
     try:
         pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file_bytes))
         text = ""
@@ -18,38 +18,38 @@ def extract_text_from_pdf(pdf_file_bytes):
                 text += page_text + "\n"
         return text.strip() or "No text could be extracted from the PDF."
     except Exception as e:
-        return f"Error reading PDF: {str(e)}"
+        return f"Error reading PDF: {e}"
 
 def extract_text_from_docx(docx_file_bytes):
-    """Extract text from a DOCX file given as bytes."""
+    """Extract text from DOCX bytes."""
     try:
         doc = Document(io.BytesIO(docx_file_bytes))
         text = "\n".join(para.text for para in doc.paragraphs)
         return text.strip() or "No text could be extracted from the DOCX file."
     except Exception as e:
-        return f"Error reading DOCX: {str(e)}"
+        return f"Error reading DOCX: {e}"
 
 def parse_cv(file, job_description):
-    """Analyze a CV (PDF or DOCX) against a job description and generate a report."""
+    """Analyze the CV (PDF or DOCX) against the job description and return an analysis report."""
     if file is None:
         return "Please upload a CV file."
-
+    
     file_ext = file.name.split(".")[-1].lower()
     try:
         file_bytes = file.read()
     except Exception as e:
-        return f"Error reading the uploaded file: {str(e)}"
-
+        return f"Error reading the uploaded file: {e}"
+    
     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."
-
-    if text.startswith("Error reading"):
-        return text  # Return error from extraction if any.
-
+    
+    if text.startswith("Error"):
+        return text  # Return extraction error if any.
+    
     prompt = (
         f"Analyze the following CV against the provided job description. "
         f"Provide a summary, an assessment of fit, and a score from 0 to 10.\n\n"
@@ -60,33 +60,23 @@ def parse_cv(file, job_description):
     try:
         response = client.text_generation(prompt, max_tokens=512)
     except Exception as e:
-        return f"Error during CV analysis: {str(e)}"
+        return f"Error during CV analysis: {e}"
     
     return response
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    """
-    Chatbot response generator that interacts with a conversational model.
-    """
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    """Generate a chatbot response based on the conversation history and parameters."""
     messages = [{"role": "system", "content": system_message}]
-
     for user_msg, bot_msg in history:
         if user_msg:
             messages.append({"role": "user", "content": user_msg})
         if bot_msg:
             messages.append({"role": "assistant", "content": bot_msg})
-
     messages.append({"role": "user", "content": message})
+    
     response = ""
-
     try:
+        # Stream response tokens from the chat completion endpoint.
         for message_chunk in client.chat_completion(
             messages,
             max_tokens=max_tokens,
@@ -98,7 +88,7 @@ def respond(
             response += token
             yield response
     except Exception as e:
-        yield f"Error during chat generation: {str(e)}"
+        yield f"Error during chat generation: {e}"
 
 # Build the Gradio interface
 demo = gr.Blocks()
@@ -107,27 +97,22 @@ with demo:
     gr.Markdown("## AI-powered CV Analyzer and Chatbot")
     
     with gr.Tab("Chatbot"):
+        # Set type="messages" to use the OpenAI-style message format.
         chat_interface = gr.ChatInterface(
             respond,
+            type="messages",
             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)",
-                ),
+                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) and provide the job description to receive a professional analysis and suitability score."
-        )
-        file_input = gr.File(label="Upload CV", type="file")
+        gr.Markdown("### Upload your CV (PDF or DOCX) and provide the job description to receive a professional analysis and suitability score.")
+        # Use type="binary" for the file component.
+        file_input = gr.File(label="Upload CV", type="binary")
         job_desc_input = gr.Textbox(label="Job Description", lines=5)
         output_text = gr.Textbox(label="CV Analysis Report", lines=10)
         analyze_button = gr.Button("Analyze CV")