Update app.py

app.py

@@ -1,64 +1,43 @@
-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
+st.set_page_config(page_title="Resume Validator", layout="centered", page_icon="📄")
+st.title("📄 AI Resume Validator")
+st.write("Upload your resume and get instant feedback on its quality and suggestions to improve.")
+uploaded_file = st.file_uploader("Upload Resume (PDF or DOCX)", type=["pdf", "docx"])
+if uploaded_file:
+    if uploaded_file.name.endswith('.pdf'):
+        from pdfminer.high_level import extract_text
+        resume_text = extract_text(uploaded_file)
+    else:
+        import docx2txt
+        resume_text = docx2txt.process(uploaded_file)
+
+    st.markdown("### 📃 Extracted Resume Text")
+    st.code(resume_text)
+template = """
+You are an expert HR recruiter.
+
+Here is the content of a resume:
+{resume_text}
+
+Evaluate the resume on the following criteria:
+1. Clarity and grammar
+2. Relevance of skills and keywords
+3. Structure (sections like Education, Experience, Projects, etc.)
+4. Overall impact
+
+Provide:
+- A rating out of 10
+- Key strengths
+- Weaknesses
+- Actionable suggestions to improve
 """
-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()
+prompt = PromptTemplate(input_variables=["resume_text"], template=template)
+llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.3", temperature=0.5)
+model = ChatHuggingFace(llm=llm)
+chain = LLMChain(llm=model, prompt=prompt)
+
+if st.button("Validate Resume"):
+    with st.spinner("Analyzing..."):
+        result = chain.run(resume_text=resume_text)
+        st.success("✅ Resume Analysis Completed")
+        st.markdown("### 📊 Feedback")
+        st.write(result)