fine tuning ml models with chat output

tharoosha · Oct 28, 2023 · a49ed6c · a49ed6c
1 parent 40ebab9
commit a49ed6c
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Showing 9 changed files with 96 additions and 71 deletions.
diff --git a/backend/controllers/mlController.js b/backend/controllers/mlController.js
@@ -118,49 +118,7 @@ export async function speech_to_text(req, res) {
   }
 }
 
-// /** GET: http://localhost:5001/api/youtube_videos */
-// /** 
-//  * @param : {
-//   "mood" : "Happiness",
-// } 
-// */
-// // Define the controller function to execute the Python script
-// export async function video_predict(req, res) {
-//   try {
-//     const { mood } = req.body;
-
-//     // Spawn the Python script as a child process
-//     const pythonProcess = spawn("/usr/src/app/venv/bin/python3", ["../ml_models/recommanded_system/combinedScript.py",mood,]);
-
-//     let output = "";
-
-//     // Listen for data events from the Python script's stdout
-//     pythonProcess.stdout.on("data", (data) => {
-//       output += data.toString();
-//     });
-
-//     // Listen for the 'close' event to handle the completion of the Python script
-//     pythonProcess.on("close", (code) => {
-//       if (code === 0) {
-//         try {
-//           const result = JSON.parse(output);
-//           res.status(200).json(result);
-//         } catch (error) {
-//           res.status(500).json({ error: "Failed to parse JSON response" });
-//         }
-//       } else {
-//         res.status(500).json({ error: "Python script exited with an error" });
-//       }
-//     });
-
-//     // Handle any errors during the execution of the Python script
-//     pythonProcess.on("error", (error) => {
-//       res.status(500).json({ error: error.message });
-//     });
-//   } catch (error) {
-//     res.status(500).json({ error: error.message });
-//   }
-// }
+
 
 /** POST: http://localhost:5001/api/emotion_analyzer */
 /** 
@@ -170,10 +128,10 @@ export async function speech_to_text(req, res) {
 */
 
 export async function emotion_analyzer(req, res) {
-  const { message } = req.body;
+  const { tweet } = req.body;
   try {
-    console.log(message)
-    const process = spawn("python3", ["../backend/ml_models/emotion_detection/emotionScript.py", message,]);
+    console.log(tweet)
+    const process = spawn("python3", ["../backend/ml_models/emotion_detection/emotionScript.py", tweet,]);
 
     let emotion = ""
     process.stdout.on("data", (data) => {
@@ -192,7 +150,7 @@ export async function emotion_analyzer(req, res) {
           // // # Extract the "emotion" field
           // const emotion = jsonData.emotion;
           const jsonData = JSON.parse(emotion);
-          const joy = jsonData.emotion;
+          // const joy = jsonData.emotion;
           // console.log(joy);
           res.status(200).send(jsonData);
         } catch (error) {

diff --git a/backend/ml_models/.gitignore b/backend/ml_models/.gitignore
@@ -1 +1,2 @@
-config.py
+config.py
+.env
diff --git a/backend/ml_models/Chatbot/ChatbotKB.ipynb b/backend/ml_models/Chatbot/ChatbotKB.ipynb
@@ -18,6 +18,29 @@
     "# ! pip3 install langchain==0.0.218\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "api_key = os.getenv(\"OPENAI_API_KEY\")\n",
+    "print(api_key)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -286,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.6"
   },
   "orig_nbformat": 4
  },

diff --git a/backend/ml_models/Chatbot/chatbotkb.py b/backend/ml_models/Chatbot/chatbotkb.py
@@ -7,13 +7,11 @@
 from llama_index import ServiceContext
 import backoff
 
-# from dotenv import load_dotenv
+from dotenv import load_dotenv
 
-# load_dotenv()
-# load
+load_dotenv()
 
-# os.environ['OPENAI_API_KEY'] = "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"
-openai.api_key = "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"
+openai.api_key = os.getenv("OPENAI_API_KEY")
 
 def create_Index(path):
     max_input = 4096

diff --git a/backend/ml_models/emotion_detection/emotionDetection.ipynb b/backend/ml_models/emotion_detection/emotionDetection.ipynb
@@ -3133,6 +3133,30 @@
     "OpenAI API"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set up your OpenAI API credentials\n",
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "api_key = os.getenv(\"OPENAI_API_KEY\")\n",
+    "print(api_key)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 59,
@@ -3141,9 +3165,7 @@
    "source": [
     "import openai\n",
     "\n",
-    "# Set up your OpenAI API credentials\n",
-    "# openai.api_key = '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'\n",
-    "openai.api_key = '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'\n",
+    "\n",
     "\n",
     "\n",
     "def get_completion(prompt, model=\"gpt-3.5-turbo\"):\n",
@@ -3439,7 +3461,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.6"
   },
   "orig_nbformat": 4
  },

diff --git a/backend/ml_models/emotion_detection/emotionScript.py b/backend/ml_models/emotion_detection/emotionScript.py
@@ -11,6 +11,8 @@
 import backoff
 import sys
 import json
+from dotenv import load_dotenv
+import os
 
 def get_tweet(data):
     texts = [x['text'] for x in data]
@@ -144,7 +146,12 @@ def makePrediction(tweet, model, tokenizer):
 
 # Set up your OpenAI API credentials
 # openai.api_key = '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'
-openai.api_key = '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'
+# Set up your OpenAI API credentials
+
+
+load_dotenv()
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
 
 def get_completion(prompt, model="gpt-3.5-turbo"):
 # def get_completion(prompt, model="text-davinci-002"):
@@ -230,8 +237,8 @@ def answer(tweet):
     # # getInstantEmotion(['I am feeling sad'], model, tokenizer)
 
     # # print(getInstantEmotion(['he has broufgt flowers to see me '], model, tokenizer))
-    # answer(input)
-    answer('he has brought flowers to see me')
+    answer(input)
+    # answer('he has brought flowers to see me')
 
     # # Call the main function with the input
     # response = getInstantEmotion(list(input), model, tokenizer)

diff --git a/backend/ml_models/requirements.txt b/backend/ml_models/requirements.txt
@@ -28,4 +28,6 @@ sentence_transformers
 # gradio==3.14.0
 SpeechRecognition==3.8.1
 # mtranslate==1.8
-# gTTS==2.3.0
+# gTTS==2.3.0
+
+python-dotenv
diff --git a/backend/ml_models/temp_audio.wav b/backend/ml_models/temp_audio.wav
diff --git a/frontend/src/pages/AI_Assistant.js b/frontend/src/pages/AI_Assistant.js
@@ -65,13 +65,17 @@ const AI_Assistant = () => {
             const audioFile = new File([audioBlob], 'userVoice.wav', { type: 'audio/wav' });
             formData.append('audio', audioFile);
 
-            console.log(formData.get('audio'));
+            const audio = formData.get('audio');
+            console.log(audio);
+
             // Send the audio data to the Node.js backend
             axios
                .post('http://localhost:5001/api/voice-input', formData)
                .then((response) => {
                   console.log(response.data.result);
-                  const updatedChatLogWithVoice = [...chatLog, { user: 'User', message: response.data.result }];
+                  let data = response.data.result
+                  data = String(data)
+                  const updatedChatLogWithVoice = [...chatLog, { user: 'User', message: data }];
                   setChatLog(updatedChatLogWithVoice);
                   // setMessage(response.data.result);
 
@@ -82,13 +86,22 @@ const AI_Assistant = () => {
                   axios
                      .post('http://localhost:5001/api/analyze', { message: voice_message })
                      .then((response) => {
-                        const updatedChatLogWithAI = [...chatLog, { user: 'AI_Consultant', message: response.data.result }];
+                        const updatedChatLogWithAI = [...chatLog, { user: 'User', message: voice_message }, { user: 'AI_Consultant', message: response.data.result }];
                         setChatLog(updatedChatLogWithAI);
                         setResponse(response.data.result);
                      })
                      .catch((error) => console.error(error));
 
-                  console.log('chatLog');
+                  axios
+                     .post('http://localhost:5001/api/emotion_analyze', { message: voice_message })
+                     .then((response) => {
+                        setEmotion(response.data.emotion);
+                        // console.log(response.data.emotion)
+                     })
+                     .catch((error) => console.error(error));
+                  // console.log(chatLog);
+                  console.log(emotion);
+
                })
                .catch((error) => console.error(error));
          };
@@ -114,21 +127,22 @@ const AI_Assistant = () => {
             const updatedChatLogWithAI = [...updatedChatLog, { user: 'AI_Consultant', message: response.data.result }];
             setChatLog(updatedChatLogWithAI);
             setResponse(response.data.result);
+            // console.log(response.data.result)
          })
          .catch((error) => console.error(error));
 
-      console.log(message)
+
       axios
          .post('http://localhost:5001/api/emotion_analyze', { message: message })
          .then((response) => {
-            setEmotion(response.data);
-            // console.log(response.data)
+            setEmotion(response.data.emotion);
+            // console.log(response.data.emotion)
          })
          .catch((error) => console.error(error));
       // Clear the input field after submitting
       setMessage('');
-      console.log(chatLog);
-      console.log(emotion);
+      // console.log(chatLog);
+      // console.log(emotion);
    };
 
    return (