Update app.py

app.py

@@ -4,39 +4,28 @@ from PIL import Image
 import PyPDF2
 import tempfile
 import os
+from google.api_core import exceptions
 from dotenv import load_dotenv
 import time
 from gtts import gTTS
 import base64
-import requests  # Added for Hugging Face API requests
-from google.api_core import exceptions
 
 load_dotenv()
 
-# Configure the Gemini AI model for text analysis
-gemini_api_key = os.getenv("GEMINI_API_KEY")
-if not gemini_api_key:
+# Load the API key from the environment variable
+api_key = os.getenv("GEMINI_API_KEY")
+if not api_key:
     st.error("Gemini API key not found. Please set the GEMINI_API_KEY environment variable.")
     st.stop()
 
-genai.configure(api_key=gemini_api_key)
-gemini_model = genai.GenerativeModel('gemini-1.5-flash')
-
-# Configure the Hugging Face model for image analysis
-huggingface_api_key = os.getenv("HUGGINGFACE_API_KEY")
-if not huggingface_api_key:
-    st.error("Hugging Face API key not found. Please set the HUGGINGFACE_API_KEY environment variable.")
-    st.stop()
-
-HUGGINGFACE_API_URL = os.getenv("HUGGINGFACE_API_URL")
-if not HUGGINGFACE_API_URL:
-    st.error("Hugging Face API URL not found. Please set the HUGGINGFACE_API_URL environment variable.")
-    st.stop()
+# Configure the Gemini API
+genai.configure(api_key=api_key)
+model = genai.GenerativeModel('gemini-1.5-flash')  # Initialize the model
 
 MAX_RETRIES = 3
 RETRY_DELAY = 2  # seconds
 
-# Dictionary for language support (including Urdu)
+# Add Chinese (Simplified) to language support
 LANGUAGES = {
     "English": "en",
     "Spanish": "es",
@@ -44,10 +33,11 @@ LANGUAGES = {
     "German": "de",
     "Italian": "it",
     "Portuguese": "pt",
-    "Urdu": "ur"
+    "Urdu": "ur",
+    "Chinese (Simplified)": "zh-cn"
 }
 
-def analyze_text_report(content, lang):
+def analyze_medical_report(content, content_type, lang):
     prompt = "Analyze this medical report concisely. Provide key findings, diagnoses, and recommendations:"
     
     # Adjust prompt language if not English
@@ -58,13 +48,18 @@ def analyze_text_report(content, lang):
             "de": "Analysieren Sie diesen medizinischen Bericht kurz und prägnant. Geben Sie wichtige Ergebnisse, Diagnosen und Empfehlungen an:",
             "it": "Analizza questo rapporto medico in modo conciso. Fornisci risultati chiave, diagnosi e raccomandazioni:",
             "pt": "Analise este relatório médico de forma concisa. Forneça os principais resultados, diagnósticos e recomendações:",
-            "ur": "اس طبی رپورٹ کا مختصر تجزیہ کریں۔ اہم نتائج، تشخیصات، اور سفارشات فراہم کریں:"
+            "ur": "اس طبی رپورٹ کا مختصر تجزیہ کریں۔ اہم نتائج، تشخیصات، اور سفارشات فراہم کریں:",
+            "zh-cn": "简明分析此医疗报告。提供关键发现、诊断和建议:"
         }
         prompt = translations.get(lang, prompt)
     
     for attempt in range(MAX_RETRIES):
         try:
-            response = gemini_model.generate_content(f"{prompt}\n\n{content}")
+            if content_type == "image":
+                response = model.generate_content([prompt, content])
+            else:  # text
+                response = model.generate_content(f"{prompt}\n\n{content}")
+            
             return response.text
         except exceptions.GoogleAPIError as e:
             if attempt < MAX_RETRIES - 1:
@@ -72,104 +67,40 @@ def analyze_text_report(content, lang):
                 time.sleep(RETRY_DELAY)
             else:
                 st.error(f"Failed to analyze the report after {MAX_RETRIES} attempts. Error: {str(e)}")
-                return fallback_analysis(content, "text")
+                return fallback_analysis(content, content_type)
 
-def analyze_image_report(image_path, lang):
-    headers = {
-        "Authorization": f"Bearer {huggingface_api_key}",
-        "Content-Type": "application/octet-stream"
-    }
+def generate_tts_audio(text, lang_code):
+    # Generate TTS audio from the provided text and language code
+    tts = gTTS(text=text, lang=lang_code)
     
-    for attempt in range(MAX_RETRIES):
-        try:
-            with open(image_path, "rb") as img_file:
-                image_data = img_file.read()
-                response = requests.post(HUGGINGFACE_API_URL, headers=headers, data=image_data)
-            
-            if response.status_code == 200:
-                result = response.json()
-                # Parse the response based on the model's output structure
-                analysis = ""
-                if isinstance(result, list):
-                    for condition in result:
-                        label = condition.get('label', 'Unknown')
-                        score = condition.get('score', 0)
-                        analysis += f"{label}: {score:.2f}\n"
-                elif isinstance(result, dict):
-                    for key, value in result.items():
-                        analysis += f"{key}: {value:.2f}\n"
-                else:
-                    st.warning("Unexpected response format from Hugging Face API.")
-                    return fallback_analysis(None, "image")
-                return analysis
-            elif response.status_code == 503:
-                # Model is loading
-                st.warning("Model is loading. Waiting for 30 seconds before retrying...")
-                time.sleep(30)
-                continue
-            else:
-                st.warning(f"Hugging Face API returned status code {response.status_code}: {response.text}")
-                if attempt < MAX_RETRIES - 1:
-                    st.warning(f"Retrying in {RETRY_DELAY} seconds... (Attempt {attempt + 1}/{MAX_RETRIES})")
-                    time.sleep(RETRY_DELAY)
-                else:
-                    st.error(f"Failed to analyze the image after {MAX_RETRIES} attempts.")
-                    return fallback_analysis(None, "image")
-        except Exception as e:
-            if attempt < MAX_RETRIES - 1:
-                st.warning(f"An error occurred: {str(e)}. Retrying in {RETRY_DELAY} seconds... (Attempt {attempt + 1}/{MAX_RETRIES})")
-                time.sleep(RETRY_DELAY)
-            else:
-                st.error(f"Failed to analyze the image after {MAX_RETRIES} attempts. Error: {str(e)}")
-                return fallback_analysis(None, "image")
-
-def fallback_analysis(content, content_type):
-    st.warning("Using fallback analysis method due to API issues.")
-    if content_type == "image":
-        return "Unable to analyze the image due to API issues. Please try again later or consult a medical professional for accurate interpretation."
-    else:  # text
-        word_count = len(content.split()) if content else 0
-        return f"""
-        **Fallback Analysis:**
-        1. **Document Type:** Text-based medical report
-        2. **Word Count:** Approximately {word_count} words
-        3. **Content:** The document appears to contain medical information, but detailed analysis is unavailable due to technical issues.
-        4. **Recommendation:** Please review the document manually or consult with a healthcare professional for accurate interpretation.
-        5. **Note:** This is a simplified analysis due to temporary unavailability of the AI service. For a comprehensive analysis, please try again later.
-        """
+    # Save the audio to a temporary file
+    audio_path = "audio_output.mp3"
+    tts.save(audio_path)
+    
+    return audio_path
+
+def audio_player(audio_path):
+    # Display an audio player in Streamlit
+    audio_file = open(audio_path, "rb")
+    audio_bytes = audio_file.read()
+    st.audio(audio_bytes, format="audio/mp3")
 
 def extract_text_from_pdf(pdf_file):
+    # Create a PDF reader object
     pdf_reader = PyPDF2.PdfReader(pdf_file)
+    
+    # Extract text from each page
     text = ""
-    for page in pdf_reader.pages:
-        page_text = page.extract_text()
-        if page_text:
-            text += page_text
-    return text
+    for page_num in range(len(pdf_reader.pages)):
+        page = pdf_reader.pages[page_num]
+        text += page.extract_text()
 
-def generate_tts_audio(text, lang_code):
-    tts = gTTS(text=text, lang=lang_code)
-    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as tmp_file:
-        tts.save(tmp_file.name)
-        return tmp_file.name
-
-def audio_player(audio_file_path):
-    with open(audio_file_path, "rb") as audio_file:
-        audio_bytes = audio_file.read()
-        b64_audio = base64.b64encode(audio_bytes).decode()
-        audio_html = f"""
-            <audio controls>
-                <source src="data:audio/mp3;base64,{b64_audio}" type="audio/mp3">
-                Your browser does not support the audio element.
-            </audio>
-        """
-        st.markdown(audio_html, unsafe_allow_html=True)
+    return text
 
 def main():
     st.title("AI-driven Medical Report Analyzer with Multilingual Audio Feedback")
     st.write("Upload a medical report (image or PDF) for analysis")
 
-    # Language selection
     language = st.selectbox("Select language for analysis and audio feedback:", list(LANGUAGES.keys()))
     lang_code = LANGUAGES[language]
 
@@ -187,11 +118,11 @@ def main():
 
             if st.button("Analyze Image Report"):
                 with st.spinner("Analyzing the medical report image..."):
-                    analysis = analyze_image_report(tmp_file_path, lang_code)
+                    analysis = analyze_medical_report(image, "image", lang_code)
                     st.subheader("Analysis Results:")
                     st.write(analysis)
 
-                    # Generate and play audio for analysis
+                    # Generate audio of the analysis
                     audio_path = generate_tts_audio(analysis, lang_code)
                     st.write("Listen to the analysis:")
                     audio_player(audio_path)
@@ -212,16 +143,20 @@ def main():
                     with open(tmp_file_path, 'rb') as pdf_file:
                         pdf_text = extract_text_from_pdf(pdf_file)
 
-                    analysis = analyze_text_report(pdf_text, lang_code)
+                    analysis = analyze_medical_report(pdf_text, "text", lang_code)
                     st.subheader("Analysis Results:")
                     st.write(analysis)
 
-                    # Generate and play audio for analysis
+                    # Generate audio of the analysis
                     audio_path = generate_tts_audio(analysis, lang_code)
                     st.write("Listen to the analysis:")
                     audio_player(audio_path)
 
                     os.unlink(tmp_file_path)
 
+    # Footer with "Made by Shan"
+    st.markdown("---")
+    st.markdown("<p style='text-align: center;'>😎Made by Shan-Ul-Haq😎</p>", unsafe_allow_html=True)
+
 if __name__ == "__main__":
     main()