Update app.py

app.py

@@ -1,68 +1,59 @@
-import streamlit as st
 import requests
 import os
-from pytube import YouTube
 import tempfile
 from fpdf import FPDF
+import streamlit as st
+from pytube import YouTube
 
-# Set your Together.AI API key
+os.environ['TOGETHER_API_KEY'] = 'your_together_api_key_here'
 together_api_key = os.environ.get("TOGETHER_API_KEY")
-url = "https://api.together.xyz/inference"
 headers = {
     "Authorization": f"Bearer {together_api_key}",
     "Content-Type": "application/json"
 }
-model = "togethercomputer/llama-2-70b-chat"
 
 def generate_sentiment_score(input_text, parameters):
     prompt = f'''
     You are an experienced interview reviewer and consultant for a reputable company. Your role is to evaluate the sentiment displayed by job candidates during their interviews based on the transcripts of their responses.
-    The hiring team has provided you with an interview transcript and has asked you to analyze the candidate's sentiment for the following parameters: {', '.join(parameters)}. Your assessment will help the team make more informed hiring decisions and identify candidates who demonstrate genuine positive sentiment towards the role and the company.
-    The parameters to evaluate are:
-    {', '.join(parameters)}.
-    To complete this task, you will:
-    1. Carefully review the provided interview transcript.
-    2. Consider phrases, word choices, or patterns of speech that convey positive or negative sentiment for each parameter.
-    3. Based on your analysis, provide a sentiment score on a scale of 1-5 for each parameter, with 1 being extremely negative and 5 being extremely positive.
-    Provide your scores in the format: Parameter: Score.
+    The parameters to evaluate are: {', '.join(parameters)}.
+    Provide a sentiment score on a scale of 1-5 for each parameter.
     '''
     data = {
-        "model": model,
-        "prompt": f"[INST]{prompt}\n{input_text}[/INST]",
+        "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "prompt": prompt,
         "temperature": 0.0,
         "max_tokens": 1024
     }
-    response = requests.post(url, headers=headers, json=data)
-    return response.json()['output']['choices'][0]['text']
+    response = requests.post("https://api.together.xyz/inference", headers=headers, json=data)
+    if response.status_code == 200:
+        return response.json()['output']['choices'][0]['text']
+    else:
+        print(f"Error: {response.status_code} - {response.text}")
 
 def generate_detailed_feedback(input_text, parameters):
     prompt = f'''
-    As an experienced interview reviewer, provide a detailed analysis of the candidate's responses based on the following parameters: {', '.join(parameters)}. 
-    
-    Include specific examples, quotes, and adjectives from the transcript that support your analysis. Offer actionable insights and recommendations for the hiring team to make informed decisions. Summarize the candidate's overall sentiment and demeanor.
+    Provide a detailed analysis of the candidate's responses based on the following parameters: {', '.join(parameters)}.
+    Include specific examples, quotes, and adjectives from the transcript that support your analysis. Summarize the candidate's overall sentiment and demeanor.
     '''
     data = {
-        "model": model,
-        "prompt": f"[INST]{prompt}\n{input_text}[/INST]",
+        "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "prompt": prompt,
         "temperature": 0.0,
         "max_tokens": 1024
     }
-    response = requests.post(url, headers=headers, json=data)
-    return response.json()['output']['choices'][0]['text']
+    response = requests.post("https://api.together.xyz/inference", headers=headers, json=data)
+    if response.status_code == 200:
+        return response.json()['output']['choices'][0]['text']
+    else:
+        print(f"Error: {response.status_code} - {response.text}")
 
 def transcript(video_link):
     try:
         yt = YouTube(video_link)
         stream = yt.streams.filter(only_audio=True).first()
         temp_file_path = stream.download()
-        print(f"Video '{yt.title}.mp4' downloaded successfully!")
         with open(temp_file_path, 'rb') as audio_data:
-            total_transcript = requests.post(
-                "https://api.together.xyz/audio/transcribe",
-                headers=headers,
-                files={"file": audio_data}
-            ).json()["text"]
-            print("Done with the video processing\n")
+            total_transcript = (openai.Audio.transcribe("whisper-1", audio_data))["text"]
         os.remove(temp_file_path)
         return total_transcript
     except Exception as e:
@@ -74,29 +65,6 @@ def main():
     st.title("Insight Hire")
     st.write("Analyze interview transcripts or videos to gain valuable insights into candidate sentiment.")
 
-    st.sidebar.markdown("## About")
-    st.sidebar.markdown("""
-    <div style='color: #1f77b4; font-weight: bold;'>Streamline Your Interview Evaluation</div>
-    - Get data-driven sentiment scores for key parameters
-    - Identify top candidates based on sentiment analysis
-    - Make informed hiring decisions with actionable insights
-    """, unsafe_allow_html=True)
-
-    st.sidebar.markdown("<hr>", unsafe_allow_html=True)
-    st.sidebar.markdown("## Tips")
-    st.sidebar.markdown("""
-    <div style='color: #2ca02c; font-weight: bold;'>📝 Input Preparation</div>
-    - Provide clear interview transcripts or valid video links
-    - Specify relevant parameters for sentiment analysis
-    """, unsafe_allow_html=True)
-
-    st.sidebar.markdown("<hr>", unsafe_allow_html=True)
-    st.sidebar.markdown("## About Me")
-    st.sidebar.markdown("""
-    <div style='color: #d62728; font-weight: bold;'>👋 Hi, I'm Dhruv!</div>
-    I want to make a real impact in the field of AI/ML . My main interest lies in model building and deployment. I'm passionate about leveraging cutting-edge technologies to solve real-world problems.
-    """, unsafe_allow_html=True)
-
     input_option = st.radio("Select input type", ("Text", "YouTube Video Link"))
 
     input_text = ""
@@ -120,7 +88,6 @@ def main():
                 sentiment_scores = generate_sentiment_score(input_text, parameters)
                 sentiment_scores = sentiment_scores.strip().split("\n")
                 st.subheader("Sentiment Scores")
-                valid_scores = []
                 for score in sentiment_scores:
                     if ":" in score:
                         param, score_value = score.split(":")
@@ -129,7 +96,6 @@ def main():
                         if param in parameters:
                             try:
                                 score_value = float(score_value.split("/")[0].strip())
-                                valid_scores.append((param, score_value))
                                 if score_value >= 4:
                                     color = "#2ca02c"
                                 elif score_value >= 3:
@@ -145,21 +111,26 @@ def main():
                     detailed_feedback = generate_detailed_feedback(input_text, parameters)
                     st.subheader("Detailed Feedback")
                     st.write(detailed_feedback)
+
                     temp_txt_path = tempfile.mktemp(suffix=".txt")
                     with open(temp_txt_path, 'w') as f:
                         f.write(detailed_feedback)
+
                     st.download_button(
                         label="Download Detailed Feedback as .txt",
                         data=open(temp_txt_path, 'r').read(),
                         file_name="detailed_feedback.txt",
                         mime="text/plain"
                     )
+
                     pdf = FPDF()
                     pdf.add_page()
                     pdf.set_font("Arial", size=12)
                     pdf.multi_cell(0, 10, detailed_feedback)
+
                     temp_pdf_path = tempfile.mktemp(suffix=".pdf")
                     pdf.output(temp_pdf_path)
+
                     with open(temp_pdf_path, "rb") as f:
                         st.download_button(
                             label="Download Detailed Feedback as .pdf",
@@ -174,4 +145,7 @@ if __name__ == "__main__":
     main()
 
 
+   
+
+